diff --git "a/data/data/single_agent/test_reference_conversations.jsonl" "b/data/data/single_agent/test_reference_conversations.jsonl" new file mode 100644--- /dev/null +++ "b/data/data/single_agent/test_reference_conversations.jsonl" @@ -0,0 +1,292 @@ +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"forest_loss_support\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"forest_loss_support\"}}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0....\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0.618845, \"pre_mean\": 0.728297}}, \"sar\": {\"VH\": {\"delta_db\": 0.133387}, \"VH_minus_VV\": {\"delta\": 0.035756, \"post\": -6.57021, \"pre\": -6.605965}, \"VV\": {\"delta_db\": 0.097631}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}, \"classific...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `forest_loss` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"positive\":\"deforestation\",\"negative\":\"no_deforestation\"},\"rule\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0....\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=19e09b930088]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"negative\": \"no_deforestation\", \"positive\": \"deforestation\"}, \"question\": \"question_1\", \"rule\": \"forest_loss\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}, \"class_1_payload\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"ndvi_drop\": 0.109452, \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [], \"label\": \"deforestation\", \"predicted_class\": \"deforestation\", \"rule\": \"forest_loss\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 81, "id": "deforestation/000149/forest_loss_detection", "dataset_id": "deforestation/000149/forest_loss_detection", "sample_id": "000149", "question_type": "forest_loss_detection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "19e09b930088413fa8cd78304db38404a45cb9c46492f6031cc7feda3281a11f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\",\"needs\":[\"question_1\",\"forest_mask_1\",\"optical_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\", \\\"optical\\\": \\\"scene_2\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\",\"optical\":\"scene_2\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\", \"optical\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `forest_loss_patches` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"forest_loss_patches\",\"filters\":{\"event_year\":\"2022\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"area_m2\": 897265.995508, \"component_count\": 90, \"event_year\": 2022, \"largest_patch_pixels\": 273, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 1007, \"target\": \"forest_loss_patches\", \"total_area_ha\": 89.7266}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"event_year\": \"2022\"}, \"question\": \"question_1\", \"target\": \"forest_loss_patches\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"area_m2\": 897265.995508, \"component_count\": 90, \"event_year\": 2022, \"largest_patch_pixels\": 273, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 1007, \"target\": \"forest_loss_patches\", \"total_area_ha\": 89.7266}, \"objects_1_payload\": {\"area_m2\": 897265.995508, \"component_count\": 90, \"crs\": \"EPSG:4326\", \"event_year\": 2022, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"component_count\": 90,...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The target `forest_loss` is available in the work context; measure area in `hectares`.\",\"action\":{\"tool\":\"MeasureArea\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"target\":\"forest_loss\",\"unit\":\"hectares\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"measured area `area_1`\"\n- latest_handle = \"area_1\"\n- latest_tool = \"MeasureArea\"\n- latest_tool_ok = true\n- area_1_evidence = {\"area\": 89.7266, \"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 897265.995508, \"component_count\": 90, \"event_year\": 2022, \"largest_patch_pixels\": 273, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 1007, \"target\": \"forest_loss_patches\", \"total_area_ha\": 89.7266}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"forest_loss\", \"unit\": \"hectares\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"area_1_answer_ref\": \"ANSWER_JSON:area_1\", \"area_1_evidence\": {\"area\": 89.7266, \"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"target\": \"forest_loss\", \"unit\": \"hectares\"}, \"area_1_payload\": {\"area\": 89.7266, \"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"input_object_summary\": {\"component_count\": 90, \"largest_patch_pixels\": 273, \"target\": \"forest_loss_patches\"}, \"source\": {\"area_m2\": 897265.995508, \"component_count\": 90, \"crs\": \"EPSG:4326\", \"event_year\": 2022, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"component_count\": 90, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 273, \"loss_pixel_count\": 1007, \"mask_kind\": \"e...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_hansen\\\", \\\"criterion\\\": \\\"validation\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_hansen\",\"criterion\":\"validation\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0.618845, \"pre_mean\": 0.728297}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- area_1_evidence = {\"area\": 89.7266, \"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 897265.995508, \"component_count\": 90, \"event_year\": 2022, \"largest_patch_pixels\": 273, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 1007, \"target\": \"forest_loss_patches\", \"total_area_ha\": 89.7266}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=df7604e0a57d]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0.618845, \"pre_mean\": 0.728297}}}, \"compare_1_payload\": {\"classification_rules\": [], \"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"hansen\": {\"alert_overlap_with_change_region\": null, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 89.7266, \"area_m2\": 897265.995508, \"component_count\": 90, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 2...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 82, "id": "deforestation/000149/area_estimation", "dataset_id": "deforestation/000149/area_estimation", "sample_id": "000149", "question_type": "area_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "MeasureArea", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "df7604e0a57d2362bfeb4d2474382aea7c42c59532b8e386150b3c9fe2ffd639"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_timeseries_1 [optical_timeseries] - Sentinel-2 temporal NDVI/NBR series across pre/post epochs.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - Sentinel-1 VH/VV time series across pre/post epochs.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_timeseries_1\",\"sar_timeseries_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_timeseries_1\",\"role\":\"ndvi_nbr\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_timeseries_1\", \"question\": \"question_1\", \"role\": \"ndvi_nbr\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_timeseries_1\",\"role\":\"vh\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_timeseries_1\", \"question\": \"question_1\", \"role\": \"vh\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_timeseries` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_timeseries\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_timeseries\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `dominant_clearing_interval` over `pre_post_epochs`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"change\":\"dominant_clearing_interval\",\"window\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.239834, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=03eede716eaf]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"dominant_clearing_interval\", \"question\": \"question_1\", \"window\": \"pre_post_epochs\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_evidence\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.239834, \"signal_used\": \"multi\"}, \"change_1_payload\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 89.7266, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"medium\", \"drop_magnitude\": 0.239834, \"epoch_series\": [{\"NBR\": 0.595941, \"NDVI\": 0.804358, \"epoch\": \"pre_3\"}, {\"NBR\": 0.591835, \"NDVI\": 0.774067, \"epoch\": \"pre_2\"}, {\"NBR\": 0.498153, \"NDVI\": 0.581075, \"epoch\": \"po...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `change_window` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"change_1\",\"labels\":{\"none\":\"null\",\"changed\":\"start_epoch_to_end_epoch\"},\"rule\":\"change_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"change_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"medium\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"domina...\n- class_1_summary = \"classified change `class_1`\"\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.239834, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=03eede716eaf]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"changed\": \"start_epoch_to_end_epoch\", \"none\": \"null\"}, \"question\": \"question_1\", \"rule\": \"change_window\", \"work\": \"change_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"change_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"medium\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 89.7266, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"medium\", \"drop_magnitude\": 0.239834, \"epoch_series\": [{\"NBR\": 0.595941, \"NDVI\": 0.804358, \"epoch\": \"pre_3\"}, {\"NBR\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 83, "id": "deforestation/000149/change_window_estimation", "dataset_id": "deforestation/000149/change_window_estimation", "sample_id": "000149", "question_type": "change_window_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "DetectChange", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "03eede716eaf3ae3f67df01ebcc79f28111464b7b5fdb94986fde25d4e2b1f84"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_false_positive` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_false_positive\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_false_positive\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"cause_attribution\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"cause_attribution\"}}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0....\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0.618845, \"pre_mean\": 0.728297}}, \"sar\": {\"VH\": {\"delta_db\": 0.133387}, \"VH_minus_VV\": {\"delta\": 0.035756, \"post\": -6.57021, \"pre\": -6.605965}, \"VV\": {\"delta_db\": 0.097631}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}, \"classificat...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `false_positive_type` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"deforestation\":true,\"plantation_harvest\":true,\"water_flood_edge\":true,\"burn_bare_soil\":true,\"agriculture\":true,\"no_change\":true},\"rule\":\"false_positive_type\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"nbr_drop\": 0.119288, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"hansen\": {\"loss_area_ha\": 89.7266, \"treecover2000_mean_pct\": 90.896591}, \"optical\": {\"NBR\": {\"drop\": 0.119288, \"post_mean\": 0.362257, \"pre_mean\": 0.481544}, \"NDVI\": {\"drop\": 0.109452, \"post_mean\": 0....\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7b7c5ab7240e]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"agriculture\": true, \"burn_bare_soil\": true, \"deforestation\": true, \"no_change\": true, \"plantation_harvest\": true, \"water_flood_edge\": true}, \"question\": \"question_1\", \"rule\": \"false_positive_type\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"nbr_drop\": 0.119288, \"ndvi_drop\": 0.109452, \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"]}, \"class_1_payload\": {\"cause\": \"deforestation\", \"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 89.7266, \"nbr_drop\": 0.119288, \"ndvi_drop\": 0.109452, \"rules_fired\": [\"hansen_loss>=0.5ha(89.7)\", \"treecover2000>=50%(90.9)\", \"ndvi_drop_in_loss_region(0.11)\"], \"sar_vh_delta_db\": 0.133387}, \"false_positive_flags\": [],...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 84, "id": "deforestation/000149/false_positive_rejection", "dataset_id": "deforestation/000149/false_positive_rejection", "sample_id": "000149", "question_type": "false_positive_rejection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7b7c5ab7240e7f35b4c6c89830847a4ac1d71a5d07a0694288ded5cdd184963b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover2000 raster for the AOI.; refs: treecover2000.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\"} into a `baseline_canopy` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\"},\"mode\":\"baseline_canopy\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\"}, \"mode\": \"baseline_canopy\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"treecover_mean_pct\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"treecover_mean_pct\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 89.7266, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\"...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"treecover_mean_pct\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 89.7266, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\": \"2021\"}], \"treecover2000_mean_pct\": 90.896591}, \"stats_1_payload\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 89.7266, \"loss_year_histogram_ha\": {\"2001\": 4.900745, \"2002\": 6.950145, \"2003\": 0.801933, \"2004\": 16.751465, \"2005\": 8.197591, \"2006\": 17.731802, \"2008\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `canopy_density` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"labels\":{\"dense_forest\":\">60\",\"open_canopy\":\"30<=mean<=60\",\"sparse\":\"<30\"},\"rule\":\"canopy_density\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 90.896591}\n- class_1_summary = \"classified change `class_1`\"\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 89.7266, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\"...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=05ccddbc2d63]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"dense_forest\": \">60\", \"open_canopy\": \"30<=mean<=60\", \"sparse\": \"<30\"}, \"question\": \"question_1\", \"rule\": \"canopy_density\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 90.896591}, \"class_1_payload\": {\"predicted_class\": \"dense_forest\", \"rule\": \"canopy_density\", \"treecover2000_mean_pct\": 90.896591}, \"class_1_payload_keys\": [\"rule\", \"treecover2000_mean_pct\", \"predicted_class\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified change `class_1`\", \"tool\": \"ClassifyChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 85, "id": "deforestation/000149/baseline_canopy_assessment", "dataset_id": "deforestation/000149/baseline_canopy_assessment", "sample_id": "000149", "question_type": "baseline_canopy_assessment", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "05ccddbc2d63674edd2bdfd2047059977572a3c39817c93c69444599116f0334"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\nREGISTERED INPUTS\n- forest_loss_history_1 [forest_loss_history] - Full Hansen lossyear raster for 2001-2023 attribution.; refs: lossyear.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_loss_history_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_loss_history_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_loss_history_1\",\"role\":\"hansen\",\"time\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_loss_history_1\", \"question\": \"question_1\", \"role\": \"hansen\", \"time\": \"2001_2023\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"loss_history\\\": \\\"scene_1\\\"} into a `forest_loss_history` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"loss_history\":\"scene_1\"},\"mode\":\"forest_loss_history\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"loss_history\": \"scene_1\"}, \"mode\": \"forest_loss_history\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"loss_area_ha_by_year\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"loss_area_ha_by_year\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\": \"2021\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"loss_area_ha_by_year\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_evidence\": {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\": \"2021\"}]}, \"stats_1_payload\": {\"analysis_window\": {\"end_year\": 2023, \"start_year\": 2001}, \"loss_year_histogram_ha\": {\"2001\": 4.900745, \"2002\": 6.950145, \"2003\": 0.801933, \"2004\": 16.751465, \"2005\": 8.197591, \"2006\": 17.731802, \"2008\": 28.33505, \"2009\": 11.583495, \"2010\": 9.088695, \"2011\": 8.910479, \"2012\": 16.573565, \"2013\": 17.018922, \"2014\": 32.968595, \"2015\": 19.870146, \"2016\": 46.690607, \"2017\": 5...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `largest_loss_year` over `2001_2023`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"change\":\"largest_loss_year\",\"window\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_summary = \"detected change `change_1`\"\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 89.727, \"year\": \"2022\"}, {\"area_ha\": 52.126, \"year\": \"2019\"}, {\"area_ha\": 50.878, \"year\": \"2017\"}, {\"area_ha\": 46.691, \"year\": \"2016\"}, {\"area_ha\": 37.335, \"year\": \"2021\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=47c2eecdd845]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"largest_loss_year\", \"question\": \"question_1\", \"window\": \"2001_2023\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_payload\": {\"change\": \"largest_loss_year\", \"loss_area_ha_by_year\": {\"2001\": 4.900745, \"2002\": 6.950145, \"2003\": 0.801933, \"2004\": 16.751465, \"2005\": 8.197591, \"2006\": 17.731802, \"2008\": 28.33505, \"2009\": 11.583495, \"2010\": 9.088695, \"2011\": 8.910479, \"2012\": 16.573565, \"2013\": 17.018922, \"2014\": 32.968595, \"2015\": 19.870146, \"2016\": 46.690607, \"2017\": 50.878118, \"2018\": 10.514211, \"2019\": 52.125641, \"2020\": 21.474043, \"2021\": 37.334504, \"2022\": 89.7266, \"2023\": 16.840649}, \"loss_ha\": 89.7266, \"measurement_contract\": \"hansen_grid_area_by_year_v1\", \"tie_break_rule\": \"earliest_year\", \"window\": \"2001_2023\", \"year\": 2022}, \"change_1_payload_keys\": [\"change\", \"window\", \"year\", \"loss_ha\", \"loss_area_ha_by_year\", \"tie_break_rule\", \"measurement_contract\"], \"change_1_summary\": \"detected change `change_1`\", \"latest_handle\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 86, "id": "deforestation/000149/multi_year_loss_attribution", "dataset_id": "deforestation/000149/multi_year_loss_attribution", "sample_id": "000149", "question_type": "multi_year_loss_attribution", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "47c2eecdd845965c02fc1898e5334acc3bdd59bd294cfd299d834a127c51e04d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\nREGISTERED INPUTS\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\"} into a `forest_cross_modal` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\"},\"mode\":\"forest_cross_modal\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_cross_modal\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_vh\\\", \\\"criterion\\\": \\\"concordance\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_vh\",\"criterion\":\"concordance\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.093867, \"post_mean\": 0.500021, \"pre_mean\": 0.593888}, \"NDVI\": {\"drop\": 0.107174, \"post_mean\": 0.682038, \"pre_mean\": 0.789213}}, \"sar\": {\"VH\": {\"delta_db\": 0.133387}, \"VH_minus_VV\": {\"delta\": 0.035756, \"post\": -6.57021, \"pre\": -6.605965}, \"VV\": {\"delta_db\": 0.097631}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8c79ced1ef3a]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"optical\": {\"NBR\": {\"drop\": 0.093867, \"post_mean\": 0.500021, \"pre_mean\": 0.593888}, \"NDVI\": {\"drop\": 0.107174, \"post_mean\": 0.682038, \"pre_mean\": 0.789213}}, \"sar\": {\"VH\": {\"delta_db\": 0.133387}, \"VH_minus_VV\": {\"delta\": 0.035756, \"post\": -6.57021, \"pre\": -6.605965}, \"VV\": {\"delta_db\": 0.097631}}}, \"compare_1_payload\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.093867, \"ndvi_drop\": 0.107174, \"vh_delta_db\": 0.133387}, \"optical\": {\"NBR\": {\"drop\": 0.093867, \"per_epoch_mean\": {\"post_1\": 0.498153, \"post_2\": 0.507417, \"post_3\": 0.494495, \"pre_2\": 0.591835, \"pre_3\": 0.595941}, \"post_mean\": 0.500021, \"pre_mean\": 0.593888}, \"NDVI\": {\"drop\": 0.107174, \"per_epoch_me...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `concordance` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"concordant\":true,\"discordant\":true,\"no_change\":true},\"rule\":\"concordance\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"evidence\": {\"nbr_drop\": 0.093867, \"ndvi_drop\": 0.107174}, \"predicted_class\": \"discordant\"}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.093867, \"post_mean\": 0.500021, \"pre_mean\": 0.593888}, \"NDVI\": {\"drop\": 0.107174, \"post_mean\": 0.682038, \"pre_mean\": 0.789213}}, \"sar\": {\"VH\": {\"delta_db\": 0.133387}, \"VH_minus_VV\": {\"delta\": 0.035756, \"post\": -6.57021, \"pre\": -6.605965}, \"VV\": {\"delta_db\": 0.097631}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8c79ced1ef3a]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"concordant\": true, \"discordant\": true, \"no_change\": true}, \"question\": \"question_1\", \"rule\": \"concordance\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"evidence\": {\"nbr_drop\": 0.093867, \"ndvi_drop\": 0.107174}, \"predicted_class\": \"discordant\"}, \"class_1_payload\": {\"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.093867, \"ndvi_drop\": 0.107174, \"vh_delta_db\": 0.133387}, \"predicted_class\": \"discordant\", \"rule\": \"concordance\", \"thresholds\": {\"optical_change_ndvi_drop_gt\": 0.1, \"sar_change_vh_delta_db_lt\": -1.0}}, \"class_1_payload_keys\": [\"rule\", \"concordance\", \"predicted_class\", \"evidence\", \"thresholds\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classifie...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 87, "id": "deforestation/000149/cross_modal_concordance", "dataset_id": "deforestation/000149/cross_modal_concordance", "sample_id": "000149", "question_type": "cross_modal_concordance", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8c79ced1ef3a6d60397fc8ea69626ed13445624d7d450812324a3d12d1fb082b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_change\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"vh_delta_db\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"vh_delta_db\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": 0.133387}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"vh_delta_db\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": 0.133387}, \"stats_1_payload\": {\"canopy_class\": \"unknown\", \"loss_area_ha\": null, \"loss_year_histogram_ha\": {}, \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": 0.133387, \"treecover2000_mean_pct\": null}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"computed statistics `stats_1`\", \"tool\": \"ComputeSta...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `forest_disturbance` on `vh_delta_severity` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"hazard\":\"forest_disturbance\",\"scale\":\"vh_delta_severity\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": 0.133387, \"scale\": \"vh_delta_severity\", \"severity\": \"none\", \"units\": \"dB\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": 0.133387}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3082cca60c98]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"forest_disturbance\", \"question\": \"question_1\", \"scale\": \"vh_delta_severity\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": 0.133387, \"scale\": \"vh_delta_severity\", \"severity\": \"none\", \"units\": \"dB\"}, \"score_1_payload\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": 0.133387, \"scale\": \"vh_delta_severity\", \"severity\": \"none\", \"severity_contract\": \"vh_delta_question_thresholds_v1\", \"severity_metric\": \"post_mean_db_minus_pre_mean_db\", \"thresholds_db\": {\"mild\": \"-1 <= delta < 0\", \"moderate\": \"-3 <= delta < -1\", \"none\": \"delta >= 0\", \"severe\": \"delta < -3\"}, \"units\": \"dB\", \"vh_delta_db\": 0.133387}, \"score_1_payload_keys\": [\"hazard\", \"scale\", \"vh_delta_db\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 88, "id": "deforestation/000149/severity_proxy_numeric", "dataset_id": "deforestation/000149/severity_proxy_numeric", "sample_id": "000149", "question_type": "severity_proxy_numeric", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3082cca60c983bf275489fe507a2adb87efe3a7535c1c1bb17cf892418c62851"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"forest_loss_support\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"forest_loss_support\"}}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"dro...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"drop\": 0.040039, \"post_mean\": 0.777504, \"pre_mean\": 0.817543}}, \"sar\": {\"VH\": {\"delta_db\": -1.686005}, \"VH_minus_VV\": {\"delta\": -0.127923, \"post\": -6.45863, \"pre\": -6.330707}, \"VV\": {\"delta_db\": -1.558082}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `forest_loss` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"positive\":\"deforestation\",\"negative\":\"no_deforestation\"},\"rule\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2022-04-02 and 2022-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB...\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"dro...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=95260d2d01bb]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"negative\": \"no_deforestation\", \"positive\": \"deforestation\"}, \"question\": \"question_1\", \"rule\": \"forest_loss\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"]}, \"class_1_payload\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"ndvi_drop\": 0.040039, \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"sar_vh_delta_db\": -1.686005}, \"false_positive_flags\": [], \"label\": \"deforestation\", \"predicted_class\": \"deforestation\", \"rule\": \"forest_loss\", \"rules_fired...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 129, "id": "deforestation/000326/forest_loss_detection", "dataset_id": "deforestation/000326/forest_loss_detection", "sample_id": "000326", "question_type": "forest_loss_detection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "95260d2d01bb66ca112195286ff1ef583d8ee65d0c1641b6b92bb3ac1227abdb"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\",\"needs\":[\"question_1\",\"forest_mask_1\",\"optical_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\", \\\"optical\\\": \\\"scene_2\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\",\"optical\":\"scene_2\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\", \"optical\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `forest_loss_patches` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"forest_loss_patches\",\"filters\":{\"event_year\":\"2022\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"area_m2\": 2597034.547581, \"component_count\": 116, \"event_year\": 2022, \"largest_patch_pixels\": 1105, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 2932, \"target\": \"forest_loss_patches\", \"total_area_ha\": 259.703455}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"event_year\": \"2022\"}, \"question\": \"question_1\", \"target\": \"forest_loss_patches\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"area_m2\": 2597034.547581, \"component_count\": 116, \"event_year\": 2022, \"largest_patch_pixels\": 1105, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 2932, \"target\": \"forest_loss_patches\", \"total_area_ha\": 259.703455}, \"objects_1_payload\": {\"area_m2\": 2597034.547581, \"component_count\": 116, \"crs\": \"EPSG:4326\", \"event_year\": 2022, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"component...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The target `forest_loss` is available in the work context; measure area in `hectares`.\",\"action\":{\"tool\":\"MeasureArea\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"target\":\"forest_loss\",\"unit\":\"hectares\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"measured area `area_1`\"\n- latest_handle = \"area_1\"\n- latest_tool = \"MeasureArea\"\n- latest_tool_ok = true\n- area_1_evidence = {\"area\": 259.703455, \"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 2597034.547581, \"component_count\": 116, \"event_year\": 2022, \"largest_patch_pixels\": 1105, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 2932, \"target\": \"forest_loss_patches\", \"total_area_ha\": 259.703455}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"forest_loss\", \"unit\": \"hectares\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"area_1_answer_ref\": \"ANSWER_JSON:area_1\", \"area_1_evidence\": {\"area\": 259.703455, \"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"target\": \"forest_loss\", \"unit\": \"hectares\"}, \"area_1_payload\": {\"area\": 259.703455, \"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"input_object_summary\": {\"component_count\": 116, \"largest_patch_pixels\": 1105, \"target\": \"forest_loss_patches\"}, \"source\": {\"area_m2\": 2597034.547581, \"component_count\": 116, \"crs\": \"EPSG:4326\", \"event_year\": 2022, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"component_count\": 116, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 1105, \"loss_pixel_count...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_hansen\\\", \\\"criterion\\\": \\\"validation\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_hansen\",\"criterion\":\"validation\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2022. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"drop\": 0.040039, \"post_mean\": 0.777504, \"pre_mean\": 0.817543}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- area_1_evidence = {\"area\": 259.703455, \"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 2597034.547581, \"component_count\": 116, \"event_year\": 2022, \"largest_patch_pixels\": 1105, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 2932, \"target\": \"forest_loss_patches\", \"total_area_ha\": 259.703455}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0597b34e2dfe]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"drop\": 0.040039, \"post_mean\": 0.777504, \"pre_mean\": 0.817543}}}, \"compare_1_payload\": {\"classification_rules\": [], \"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"hansen\": {\"alert_overlap_with_change_region\": null, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2022, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 259.703455, \"area_m2\": 2597034.547581, \"component_count\": 116, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 130, "id": "deforestation/000326/area_estimation", "dataset_id": "deforestation/000326/area_estimation", "sample_id": "000326", "question_type": "area_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "MeasureArea", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0597b34e2dfe4e85294efb692c31fbe145a3e4e7d0b9ecb3f7029ded1ddbea59"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_timeseries_1 [optical_timeseries] - Sentinel-2 temporal NDVI/NBR series across pre/post epochs.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - Sentinel-1 VH/VV time series across pre/post epochs.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_timeseries_1\",\"sar_timeseries_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_timeseries_1\",\"role\":\"ndvi_nbr\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_timeseries_1\", \"question\": \"question_1\", \"role\": \"ndvi_nbr\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_timeseries_1\",\"role\":\"vh\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_timeseries_1\", \"question\": \"question_1\", \"role\": \"vh\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_timeseries` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_timeseries\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_timeseries\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `dominant_clearing_interval` over `pre_post_epochs`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"change\":\"dominant_clearing_interval\",\"window\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.132508, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=66473888cc65]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"dominant_clearing_interval\", \"question\": \"question_1\", \"window\": \"pre_post_epochs\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_evidence\": {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.132508, \"signal_used\": \"multi\"}, \"change_1_payload\": {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 259.703455, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"medium\", \"drop_magnitude\": 0.132508, \"epoch_series\": [{\"NBR\": 0.530003, \"NDVI\": 0.782153, \"epoch\": \"pre_3\"}, {\"NBR\": 0.563991, \"NDVI\": 0.818834, \"epoch\": \"pre_2\"}, {\"NBR\": 0.553796, \"NDVI\": 0.794034, \"epoch\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `change_window` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"change_1\",\"labels\":{\"none\":\"null\",\"changed\":\"start_epoch_to_end_epoch\"},\"rule\":\"change_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"change_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"confidence\": \"medium\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"change\": \"...\n- class_1_summary = \"classified change `class_1`\"\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"confidence\": \"medium\", \"drop_magnitude\": 0.132508, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=66473888cc65]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"changed\": \"start_epoch_to_end_epoch\", \"none\": \"null\"}, \"question\": \"question_1\", \"rule\": \"change_window\", \"work\": \"change_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"change_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"confidence\": \"medium\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_2\", \"start_epoch\": \"post_1\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 259.703455, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"medium\", \"drop_magnitude\": 0.132508, \"epoch_series\": [{\"NBR\": 0.530003, \"NDVI\": 0.782153, \"epoch\": \"pre_3\"},...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 131, "id": "deforestation/000326/change_window_estimation", "dataset_id": "deforestation/000326/change_window_estimation", "sample_id": "000326", "question_type": "change_window_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "DetectChange", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "66473888cc65be94059b4a7730616d7da0b1f813cd115483b95b2cbc94e5913b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2022.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_false_positive` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_false_positive\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_false_positive\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"cause_attribution\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"cause_attribution\"}}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"dro...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"drop\": 0.040039, \"post_mean\": 0.777504, \"pre_mean\": 0.817543}}, \"sar\": {\"VH\": {\"delta_db\": -1.686005}, \"VH_minus_VV\": {\"delta\": -0.127923, \"post\": -6.45863, \"pre\": -6.330707}, \"VV\": {\"delta_db\": -1.558082}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_l...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `false_positive_type` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"deforestation\":true,\"plantation_harvest\":true,\"water_flood_edge\":true,\"burn_bare_soil\":true,\"agriculture\":true,\"no_change\":true},\"rule\":\"false_positive_type\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"nbr_drop\": 0.036348, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\",...\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"], \"hansen\": {\"loss_area_ha\": 259.703455, \"treecover2000_mean_pct\": 99.533051}, \"optical\": {\"NBR\": {\"drop\": 0.036348, \"post_mean\": 0.518934, \"pre_mean\": 0.555282}, \"NDVI\": {\"dro...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ac07b81f5caa]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"agriculture\": true, \"burn_bare_soil\": true, \"deforestation\": true, \"no_change\": true, \"plantation_harvest\": true, \"water_flood_edge\": true}, \"question\": \"question_1\", \"rule\": \"false_positive_type\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"nbr_drop\": 0.036348, \"ndvi_drop\": 0.040039, \"sar_vh_delta_db\": -1.686005}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"]}, \"class_1_payload\": {\"cause\": \"deforestation\", \"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 259.703455, \"nbr_drop\": 0.036348, \"ndvi_drop\": 0.040039, \"rules_fired\": [\"hansen_loss>=0.5ha(259.7)\", \"treecover2000>=50%(99.5)\", \"ndvi_drop_in_loss_region(0.04)\", \"sar_vh_delta(-1.69dB)\"]...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 132, "id": "deforestation/000326/false_positive_rejection", "dataset_id": "deforestation/000326/false_positive_rejection", "sample_id": "000326", "question_type": "false_positive_rejection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ac07b81f5caa7192da91e3ffa17c7376ca741d96d9a4d5e2439a44d49e37f093"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover2000 raster for the AOI.; refs: treecover2000.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\"} into a `baseline_canopy` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\"},\"mode\":\"baseline_canopy\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\"}, \"mode\": \"baseline_canopy\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"treecover_mean_pct\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"treecover_mean_pct\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 259.703455, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"treecover_mean_pct\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 259.703455, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005, \"year\": \"2012\"}], \"treecover2000_mean_pct\": 99.533051}, \"stats_1_payload\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 259.703455, \"loss_year_histogram_ha\": {\"2002\": 0.885769, \"2003\": 6.111323, \"2004\": 0.354301, \"2005\": 0.354297, \"2007\": 3.099944, \"2008\": 0.177145...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `canopy_density` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"labels\":{\"dense_forest\":\">60\",\"open_canopy\":\"30<=mean<=60\",\"sparse\":\"<30\"},\"rule\":\"canopy_density\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 99.533051}\n- class_1_summary = \"classified change `class_1`\"\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 259.703455, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e34f33abe9c7]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"dense_forest\": \">60\", \"open_canopy\": \"30<=mean<=60\", \"sparse\": \"<30\"}, \"question\": \"question_1\", \"rule\": \"canopy_density\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 99.533051}, \"class_1_payload\": {\"predicted_class\": \"dense_forest\", \"rule\": \"canopy_density\", \"treecover2000_mean_pct\": 99.533051}, \"class_1_payload_keys\": [\"rule\", \"treecover2000_mean_pct\", \"predicted_class\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified change `class_1`\", \"tool\": \"ClassifyChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 133, "id": "deforestation/000326/baseline_canopy_assessment", "dataset_id": "deforestation/000326/baseline_canopy_assessment", "sample_id": "000326", "question_type": "baseline_canopy_assessment", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e34f33abe9c79a473ff669dcaffc542bb9744459ee00e0c982b687d19a28e15b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\nREGISTERED INPUTS\n- forest_loss_history_1 [forest_loss_history] - Full Hansen lossyear raster for 2001-2023 attribution.; refs: lossyear.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_loss_history_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_loss_history_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_loss_history_1\",\"role\":\"hansen\",\"time\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_loss_history_1\", \"question\": \"question_1\", \"role\": \"hansen\", \"time\": \"2001_2023\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"loss_history\\\": \\\"scene_1\\\"} into a `forest_loss_history` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"loss_history\":\"scene_1\"},\"mode\":\"forest_loss_history\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"loss_history\": \"scene_1\"}, \"mode\": \"forest_loss_history\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"loss_area_ha_by_year\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"loss_area_ha_by_year\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005, \"year\": \"2012\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"loss_area_ha_by_year\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_evidence\": {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005, \"year\": \"2012\"}]}, \"stats_1_payload\": {\"analysis_window\": {\"end_year\": 2023, \"start_year\": 2001}, \"loss_year_histogram_ha\": {\"2002\": 0.885769, \"2003\": 6.111323, \"2004\": 0.354301, \"2005\": 0.354297, \"2007\": 3.099944, \"2008\": 0.177145, \"2009\": 0.177149, \"2010\": 0.531448, \"2011\": 92.826266, \"2012\": 124.004929, \"2013\": 308.424202, \"2014\": 271.03904, \"2015\": 33.128084, \"2016\": 18.157316, \"2017\": 81.844708, \"20...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `largest_loss_year` over `2001_2023`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"change\":\"largest_loss_year\",\"window\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_summary = \"detected change `change_1`\"\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 308.424, \"year\": \"2013\"}, {\"area_ha\": 271.039, \"year\": \"2014\"}, {\"area_ha\": 259.703, \"year\": \"2022\"}, {\"area_ha\": 235.334, \"year\": \"2021\"}, {\"area_ha\": 124.005, \"year\": \"2012\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c5835118079d]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"largest_loss_year\", \"question\": \"question_1\", \"window\": \"2001_2023\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_payload\": {\"change\": \"largest_loss_year\", \"loss_area_ha_by_year\": {\"2002\": 0.885769, \"2003\": 6.111323, \"2004\": 0.354301, \"2005\": 0.354297, \"2007\": 3.099944, \"2008\": 0.177145, \"2009\": 0.177149, \"2010\": 0.531448, \"2011\": 92.826266, \"2012\": 124.004929, \"2013\": 308.424202, \"2014\": 271.03904, \"2015\": 33.128084, \"2016\": 18.157316, \"2017\": 81.844708, \"2018\": 8.769015, \"2019\": 18.512223, \"2020\": 123.026925, \"2021\": 235.334209, \"2022\": 259.703455, \"2023\": 26.661314}, \"loss_ha\": 308.424202, \"measurement_contract\": \"hansen_grid_area_by_year_v1\", \"tie_break_rule\": \"earliest_year\", \"window\": \"2001_2023\", \"year\": 2013}, \"change_1_payload_keys\": [\"change\", \"window\", \"year\", \"loss_ha\", \"loss_area_ha_by_year\", \"tie_break_rule\", \"measurement_contract\"], \"change_1_summary\": \"detected change `change_1`\", \"latest_handle\": \"change_1\",...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 134, "id": "deforestation/000326/multi_year_loss_attribution", "dataset_id": "deforestation/000326/multi_year_loss_attribution", "sample_id": "000326", "question_type": "multi_year_loss_attribution", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c5835118079d63f7430dd3adcedaea35bbdbad8e0b47a65f529e62c2e388309a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\nREGISTERED INPUTS\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\"} into a `forest_cross_modal` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\"},\"mode\":\"forest_cross_modal\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_cross_modal\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_vh\\\", \\\"criterion\\\": \\\"concordance\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_vh\",\"criterion\":\"concordance\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.109286, \"post_mean\": 0.439977, \"pre_mean\": 0.549263}, \"NDVI\": {\"drop\": 0.09166, \"post_mean\": 0.70668, \"pre_mean\": 0.798341}}, \"sar\": {\"VH\": {\"delta_db\": -1.686005}, \"VH_minus_VV\": {\"delta\": -0.127923, \"post\": -6.45863, \"pre\": -6.330707}, \"VV\": {\"delta_db\": -1.558082}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5c79df9b271b]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"optical\": {\"NBR\": {\"drop\": 0.109286, \"post_mean\": 0.439977, \"pre_mean\": 0.549263}, \"NDVI\": {\"drop\": 0.09166, \"post_mean\": 0.70668, \"pre_mean\": 0.798341}}, \"sar\": {\"VH\": {\"delta_db\": -1.686005}, \"VH_minus_VV\": {\"delta\": -0.127923, \"post\": -6.45863, \"pre\": -6.330707}, \"VV\": {\"delta_db\": -1.558082}}}, \"compare_1_payload\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.109286, \"ndvi_drop\": 0.09166, \"vh_delta_db\": -1.686005}, \"optical\": {\"NBR\": {\"drop\": 0.109286, \"per_epoch_mean\": {\"post_1\": 0.479126, \"post_2\": 0.400828, \"pre_1\": 0.553796, \"pre_2\": 0.563991, \"pre_3\": 0.530003}, \"post_mean\": 0.439977, \"pre_mean\": 0.549263}, \"NDVI\": {\"drop\": 0.09166, \"per_epoch_mea...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `concordance` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"concordant\":true,\"discordant\":true,\"no_change\":true},\"rule\":\"concordance\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"evidence\": {\"nbr_drop\": 0.109286, \"ndvi_drop\": 0.09166}, \"predicted_class\": \"discordant\"}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.109286, \"post_mean\": 0.439977, \"pre_mean\": 0.549263}, \"NDVI\": {\"drop\": 0.09166, \"post_mean\": 0.70668, \"pre_mean\": 0.798341}}, \"sar\": {\"VH\": {\"delta_db\": -1.686005}, \"VH_minus_VV\": {\"delta\": -0.127923, \"post\": -6.45863, \"pre\": -6.330707}, \"VV\": {\"delta_db\": -1.558082}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5c79df9b271b]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"concordant\": true, \"discordant\": true, \"no_change\": true}, \"question\": \"question_1\", \"rule\": \"concordance\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"evidence\": {\"nbr_drop\": 0.109286, \"ndvi_drop\": 0.09166}, \"predicted_class\": \"discordant\"}, \"class_1_payload\": {\"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.109286, \"ndvi_drop\": 0.09166, \"vh_delta_db\": -1.686005}, \"predicted_class\": \"discordant\", \"rule\": \"concordance\", \"thresholds\": {\"optical_change_ndvi_drop_gt\": 0.1, \"sar_change_vh_delta_db_lt\": -1.0}}, \"class_1_payload_keys\": [\"rule\", \"concordance\", \"predicted_class\", \"evidence\", \"thresholds\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 135, "id": "deforestation/000326/cross_modal_concordance", "dataset_id": "deforestation/000326/cross_modal_concordance", "sample_id": "000326", "question_type": "cross_modal_concordance", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5c79df9b271b4df8ebedbe48a2c683ee5c36e3b24a111eb2831792fc1b91f7cb"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_change\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"vh_delta_db\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"vh_delta_db\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.686005}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"vh_delta_db\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.686005}, \"stats_1_payload\": {\"canopy_class\": \"unknown\", \"loss_area_ha\": null, \"loss_year_histogram_ha\": {}, \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.686005, \"treecover2000_mean_pct\": null}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"computed statistics `stats_1`\", \"tool\": \"ComputeS...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `forest_disturbance` on `vh_delta_severity` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"hazard\":\"forest_disturbance\",\"scale\":\"vh_delta_severity\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.686005, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"units\": \"dB\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.686005}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c9e44bb1c64c]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"forest_disturbance\", \"question\": \"question_1\", \"scale\": \"vh_delta_severity\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.686005, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"units\": \"dB\"}, \"score_1_payload\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.686005, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"severity_contract\": \"vh_delta_question_thresholds_v1\", \"severity_metric\": \"post_mean_db_minus_pre_mean_db\", \"thresholds_db\": {\"mild\": \"-1 <= delta < 0\", \"moderate\": \"-3 <= delta < -1\", \"none\": \"delta >= 0\", \"severe\": \"delta < -3\"}, \"units\": \"dB\", \"vh_delta_db\": -1.686005}, \"score_1_payload_keys\": [\"hazard\", \"scale\", \"v...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 136, "id": "deforestation/000326/severity_proxy_numeric", "dataset_id": "deforestation/000326/severity_proxy_numeric", "sample_id": "000326", "question_type": "severity_proxy_numeric", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c9e44bb1c64c7069e1a99f48d293d41dd81547e02d4d222e2dd998b5f434af36"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2021.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"forest_loss_support\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"forest_loss_support\"}}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334,...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334, \"pre_mean\": 0.664464}}, \"sar\": {\"VH\": {\"delta_db\": -0.17847}, \"VH_minus_VV\": {\"delta\": -0.238825, \"post\": -6.890854, \"pre\": -6.652029}, \"VV\": {\"delta_db\": 0.060355}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}, \"classification_rules\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `forest_loss` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"positive\":\"deforestation\",\"negative\":\"no_deforestation\"},\"rule\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2021-04-02 and 2021-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334,...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5cd0bbeb60a]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"negative\": \"no_deforestation\", \"positive\": \"deforestation\"}, \"question\": \"question_1\", \"rule\": \"forest_loss\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}, \"class_1_payload\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"ndvi_drop\": -0.03087, \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"label\": \"deforestation\", \"predicted_class\": \"deforestation\", \"rule\": \"forest_loss\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 217, "id": "deforestation/000708/forest_loss_detection", "dataset_id": "deforestation/000708/forest_loss_detection", "sample_id": "000708", "question_type": "forest_loss_detection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5cd0bbeb60abe1e6f256c4f0e3ddb36e10b9af017fe4958e3b5f9d64f9e8965"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2021.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\",\"needs\":[\"question_1\",\"forest_mask_1\",\"optical_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\", \\\"optical\\\": \\\"scene_2\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\",\"optical\":\"scene_2\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\", \"optical\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `forest_loss_patches` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"forest_loss_patches\",\"filters\":{\"event_year\":\"2021\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"area_m2\": 754513.13994, \"component_count\": 146, \"event_year\": 2021, \"largest_patch_pixels\": 54, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 844, \"target\": \"forest_loss_patches\", \"total_area_ha\": 75.451314}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"event_year\": \"2021\"}, \"question\": \"question_1\", \"target\": \"forest_loss_patches\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"area_m2\": 754513.13994, \"component_count\": 146, \"event_year\": 2021, \"largest_patch_pixels\": 54, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 844, \"target\": \"forest_loss_patches\", \"total_area_ha\": 75.451314}, \"objects_1_payload\": {\"area_m2\": 754513.13994, \"component_count\": 146, \"crs\": \"EPSG:4326\", \"event_year\": 2021, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2021, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"component_count\": 14...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The target `forest_loss` is available in the work context; measure area in `hectares`.\",\"action\":{\"tool\":\"MeasureArea\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"target\":\"forest_loss\",\"unit\":\"hectares\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"measured area `area_1`\"\n- latest_handle = \"area_1\"\n- latest_tool = \"MeasureArea\"\n- latest_tool_ok = true\n- area_1_evidence = {\"area\": 75.451314, \"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 754513.13994, \"component_count\": 146, \"event_year\": 2021, \"largest_patch_pixels\": 54, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 844, \"target\": \"forest_loss_patches\", \"total_area_ha\": 75.451314}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"forest_loss\", \"unit\": \"hectares\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"area_1_answer_ref\": \"ANSWER_JSON:area_1\", \"area_1_evidence\": {\"area\": 75.451314, \"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"target\": \"forest_loss\", \"unit\": \"hectares\"}, \"area_1_payload\": {\"area\": 75.451314, \"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"input_object_summary\": {\"component_count\": 146, \"largest_patch_pixels\": 54, \"target\": \"forest_loss_patches\"}, \"source\": {\"area_m2\": 754513.13994, \"component_count\": 146, \"crs\": \"EPSG:4326\", \"event_year\": 2021, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2021, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"component_count\": 146, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 54, \"loss_pixel_count\": 844, \"mask_kin...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_hansen\\\", \\\"criterion\\\": \\\"validation\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_hansen\",\"criterion\":\"validation\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2021. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334, \"pre_mean\": 0.664464}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- area_1_evidence = {\"area\": 75.451314, \"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 754513.13994, \"component_count\": 146, \"event_year\": 2021, \"largest_patch_pixels\": 54, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 844, \"target\": \"forest_loss_patches\", \"total_area_ha\": 75.451314}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fdca1660c50a]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334, \"pre_mean\": 0.664464}}}, \"compare_1_payload\": {\"classification_rules\": [], \"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"hansen\": {\"alert_overlap_with_change_region\": null, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2021, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 75.451314, \"area_m2\": 754513.13994, \"component_count\": 146, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 218, "id": "deforestation/000708/area_estimation", "dataset_id": "deforestation/000708/area_estimation", "sample_id": "000708", "question_type": "area_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "MeasureArea", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "fdca1660c50a711fd5324955b2d90b9464370ba8ff79fcaec2417f95bdd0d952"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2021.tif, lossyear.tif\n- optical_timeseries_1 [optical_timeseries] - Sentinel-2 temporal NDVI/NBR series across pre/post epochs.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - Sentinel-1 VH/VV time series across pre/post epochs.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_timeseries_1\",\"sar_timeseries_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_timeseries_1\",\"role\":\"ndvi_nbr\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_timeseries_1\", \"question\": \"question_1\", \"role\": \"ndvi_nbr\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_timeseries_1\",\"role\":\"vh\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_timeseries_1\", \"question\": \"question_1\", \"role\": \"vh\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_timeseries` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_timeseries\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_timeseries\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `dominant_clearing_interval` over `pre_post_epochs`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"change\":\"dominant_clearing_interval\",\"window\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"low\", \"drop_magnitude\": 0.042264, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43d8f931b906]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"dominant_clearing_interval\", \"question\": \"question_1\", \"window\": \"pre_post_epochs\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_evidence\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"low\", \"drop_magnitude\": 0.042264, \"signal_used\": \"multi\"}, \"change_1_payload\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 75.451314, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"low\", \"drop_magnitude\": 0.042264, \"epoch_series\": [{\"NBR\": 0.580221, \"NDVI\": 0.814203, \"epoch\": \"pre_3\"}, {\"NBR\": 0.59616, \"NDVI\": 0.840834, \"epoch\": \"pre_2\"}, {\"NBR\": 0.55617, \"NDVI\": 0.818566, \"epoch\": \"post_1\"}...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `change_window` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"change_1\",\"labels\":{\"none\":\"null\",\"changed\":\"start_epoch_to_end_epoch\"},\"rule\":\"change_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"change_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"dominan...\n- class_1_summary = \"classified change `class_1`\"\n- change_1_evidence = {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"low\", \"drop_magnitude\": 0.042264, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43d8f931b906]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"changed\": \"start_epoch_to_end_epoch\", \"none\": \"null\"}, \"question\": \"question_1\", \"rule\": \"change_window\", \"work\": \"change_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"change_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"predicted_class\": \"deforestation\", \"source\": {\"best_window\": {\"end_epoch\": \"post_1\", \"start_epoch\": \"pre_2\"}, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 75.451314, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": true, \"confidence\": \"low\", \"drop_magnitude\": 0.042264, \"epoch_series\": [{\"NBR\": 0.580221, \"NDVI\": 0.814203, \"epoch\": \"pre_3\"}, {\"NBR\": 0....\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 219, "id": "deforestation/000708/change_window_estimation", "dataset_id": "deforestation/000708/change_window_estimation", "sample_id": "000708", "question_type": "change_window_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "DetectChange", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43d8f931b9064ad3b358e34ff25ecc8c332e202d74c7fea785825ab9ae44478f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2021.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_false_positive` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_false_positive\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_false_positive\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"cause_attribution\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"cause_attribution\"}}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334,...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334, \"pre_mean\": 0.664464}}, \"sar\": {\"VH\": {\"delta_db\": -0.17847}, \"VH_minus_VV\": {\"delta\": -0.238825, \"post\": -6.890854, \"pre\": -6.652029}, \"VV\": {\"delta_db\": 0.060355}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"high\", \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}, \"classification_rules\": [\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `false_positive_type` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"deforestation\":true,\"plantation_harvest\":true,\"water_flood_edge\":true,\"burn_bare_soil\":true,\"agriculture\":true,\"no_change\":true},\"rule\":\"false_positive_type\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"nbr_drop\": 0.000515, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"hansen\": {\"loss_area_ha\": 75.451314, \"treecover2000_mean_pct\": 97.666618}, \"optical\": {\"NBR\": {\"drop\": 0.000515, \"post_mean\": 0.312651, \"pre_mean\": 0.313166}, \"NDVI\": {\"drop\": -0.03087, \"post_mean\": 0.695334,...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=aed53e883ce1]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"agriculture\": true, \"burn_bare_soil\": true, \"deforestation\": true, \"no_change\": true, \"plantation_harvest\": true, \"water_flood_edge\": true}, \"question\": \"question_1\", \"rule\": \"false_positive_type\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"nbr_drop\": 0.000515, \"ndvi_drop\": -0.03087, \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"predicted_class\": \"deforestation\", \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"]}, \"class_1_payload\": {\"cause\": \"deforestation\", \"confidence\": \"high\", \"evidence\": {\"hansen_loss_area_ha\": 75.451314, \"nbr_drop\": 0.000515, \"ndvi_drop\": -0.03087, \"rules_fired\": [\"hansen_loss>=0.5ha(75.5)\", \"treecover2000>=50%(97.7)\", \"sar_vh_delta(-0.18dB)\"], \"sar_vh_delta_db\": -0.17847}, \"false_positive_flags\": [], \"predicted_cl...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 220, "id": "deforestation/000708/false_positive_rejection", "dataset_id": "deforestation/000708/false_positive_rejection", "sample_id": "000708", "question_type": "false_positive_rejection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "aed53e883ce1fb3f76696bd1c83bb2d81ba60e1a4d5c874f2bfffab85ac5c7b7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover2000 raster for the AOI.; refs: treecover2000.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\"} into a `baseline_canopy` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\"},\"mode\":\"baseline_canopy\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\"}, \"mode\": \"baseline_canopy\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"treecover_mean_pct\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"treecover_mean_pct\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 75.451314, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"treecover_mean_pct\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 75.451314, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year\": \"2016\"}], \"treecover2000_mean_pct\": 97.666618}, \"stats_1_payload\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 75.451314, \"loss_year_histogram_ha\": {\"2001\": 58.555216, \"2002\": 3.844082, \"2003\": 9.118525, \"2004\": 14.21417, \"2005\": 20.293179, \"2006\": 35.758908, \"20...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `canopy_density` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"labels\":{\"dense_forest\":\">60\",\"open_canopy\":\"30<=mean<=60\",\"sparse\":\"<30\"},\"rule\":\"canopy_density\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 97.666618}\n- class_1_summary = \"classified change `class_1`\"\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 75.451314, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7afed5fe989d]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"dense_forest\": \">60\", \"open_canopy\": \"30<=mean<=60\", \"sparse\": \"<30\"}, \"question\": \"question_1\", \"rule\": \"canopy_density\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 97.666618}, \"class_1_payload\": {\"predicted_class\": \"dense_forest\", \"rule\": \"canopy_density\", \"treecover2000_mean_pct\": 97.666618}, \"class_1_payload_keys\": [\"rule\", \"treecover2000_mean_pct\", \"predicted_class\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified change `class_1`\", \"tool\": \"ClassifyChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 221, "id": "deforestation/000708/baseline_canopy_assessment", "dataset_id": "deforestation/000708/baseline_canopy_assessment", "sample_id": "000708", "question_type": "baseline_canopy_assessment", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7afed5fe989d987ae1f564967917bd9ff42eb66af4ed49b2b6c2deeeb86f4936"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\nREGISTERED INPUTS\n- forest_loss_history_1 [forest_loss_history] - Full Hansen lossyear raster for 2001-2023 attribution.; refs: lossyear.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_loss_history_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_loss_history_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_loss_history_1\",\"role\":\"hansen\",\"time\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_loss_history_1\", \"question\": \"question_1\", \"role\": \"hansen\", \"time\": \"2001_2023\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"loss_history\\\": \\\"scene_1\\\"} into a `forest_loss_history` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"loss_history\":\"scene_1\"},\"mode\":\"forest_loss_history\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"loss_history\": \"scene_1\"}, \"mode\": \"forest_loss_history\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"loss_area_ha_by_year\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"loss_area_ha_by_year\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year\": \"2016\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"loss_area_ha_by_year\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_evidence\": {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year\": \"2016\"}]}, \"stats_1_payload\": {\"analysis_window\": {\"end_year\": 2023, \"start_year\": 2001}, \"loss_year_histogram_ha\": {\"2001\": 58.555216, \"2002\": 3.844082, \"2003\": 9.118525, \"2004\": 14.21417, \"2005\": 20.293179, \"2006\": 35.758908, \"2007\": 4.380468, \"2008\": 6.079014, \"2009\": 56.141492, \"2010\": 44.966835, \"2011\": 2.145535, \"2012\": 13.052007, \"2013\": 95.297509, \"2014\": 99.946179, \"2015\": 91.542817, \"2016\": 8...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `largest_loss_year` over `2001_2023`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"change\":\"largest_loss_year\",\"window\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_summary = \"detected change `change_1`\"\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 99.946, \"year\": \"2014\"}, {\"area_ha\": 95.298, \"year\": \"2013\"}, {\"area_ha\": 91.543, \"year\": \"2015\"}, {\"area_ha\": 89.487, \"year\": \"2017\"}, {\"area_ha\": 87.52, \"year\": \"2016\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c4fc0de02aac]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"largest_loss_year\", \"question\": \"question_1\", \"window\": \"2001_2023\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_payload\": {\"change\": \"largest_loss_year\", \"loss_area_ha_by_year\": {\"2001\": 58.555216, \"2002\": 3.844082, \"2003\": 9.118525, \"2004\": 14.21417, \"2005\": 20.293179, \"2006\": 35.758908, \"2007\": 4.380468, \"2008\": 6.079014, \"2009\": 56.141492, \"2010\": 44.966835, \"2011\": 2.145535, \"2012\": 13.052007, \"2013\": 95.297509, \"2014\": 99.946179, \"2015\": 91.542817, \"2016\": 87.519943, \"2017\": 89.486688, \"2018\": 68.657116, \"2019\": 50.330672, \"2020\": 69.282887, \"2021\": 75.451314, \"2022\": 50.956454, \"2023\": 64.634233}, \"loss_ha\": 99.946179, \"measurement_contract\": \"hansen_grid_area_by_year_v1\", \"tie_break_rule\": \"earliest_year\", \"window\": \"2001_2023\", \"year\": 2014}, \"change_1_payload_keys\": [\"change\", \"window\", \"year\", \"loss_ha\", \"loss_area_ha_by_year\", \"tie_break_rule\", \"measurement_contract\"], \"change_1_summary\": \"detected change `chang...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 222, "id": "deforestation/000708/multi_year_loss_attribution", "dataset_id": "deforestation/000708/multi_year_loss_attribution", "sample_id": "000708", "question_type": "multi_year_loss_attribution", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c4fc0de02aac166cb8f40e4436da420f0604164aecfd1621891a3ef571915527"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\nREGISTERED INPUTS\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\"} into a `forest_cross_modal` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\"},\"mode\":\"forest_cross_modal\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_cross_modal\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_vh\\\", \\\"criterion\\\": \\\"concordance\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_vh\",\"criterion\":\"concordance\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.029185, \"post_mean\": 0.559005, \"pre_mean\": 0.588191}, \"NDVI\": {\"drop\": 0.007671, \"post_mean\": 0.819847, \"pre_mean\": 0.827519}}, \"sar\": {\"VH\": {\"delta_db\": -0.17847}, \"VH_minus_VV\": {\"delta\": -0.238825, \"post\": -6.890854, \"pre\": -6.652029}, \"VV\": {\"delta_db\": 0.060355}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e4e85d61a586]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"optical\": {\"NBR\": {\"drop\": 0.029185, \"post_mean\": 0.559005, \"pre_mean\": 0.588191}, \"NDVI\": {\"drop\": 0.007671, \"post_mean\": 0.819847, \"pre_mean\": 0.827519}}, \"sar\": {\"VH\": {\"delta_db\": -0.17847}, \"VH_minus_VV\": {\"delta\": -0.238825, \"post\": -6.890854, \"pre\": -6.652029}, \"VV\": {\"delta_db\": 0.060355}}}, \"compare_1_payload\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"concordance\": \"no_change\", \"evidence\": {\"nbr_drop\": 0.029185, \"ndvi_drop\": 0.007671, \"vh_delta_db\": -0.17847}, \"optical\": {\"NBR\": {\"drop\": 0.029185, \"per_epoch_mean\": {\"post_1\": 0.55617, \"post_2\": 0.560574, \"post_3\": 0.560272, \"pre_2\": 0.59616, \"pre_3\": 0.580221}, \"post_mean\": 0.559005, \"pre_mean\": 0.588191}, \"NDVI\": {\"drop\": 0.007671, \"per_epoch_mea...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `concordance` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"concordant\":true,\"discordant\":true,\"no_change\":true},\"rule\":\"concordance\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"evidence\": {\"nbr_drop\": 0.029185, \"ndvi_drop\": 0.007671}, \"predicted_class\": \"no_change\"}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.029185, \"post_mean\": 0.559005, \"pre_mean\": 0.588191}, \"NDVI\": {\"drop\": 0.007671, \"post_mean\": 0.819847, \"pre_mean\": 0.827519}}, \"sar\": {\"VH\": {\"delta_db\": -0.17847}, \"VH_minus_VV\": {\"delta\": -0.238825, \"post\": -6.890854, \"pre\": -6.652029}, \"VV\": {\"delta_db\": 0.060355}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e4e85d61a586]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"concordant\": true, \"discordant\": true, \"no_change\": true}, \"question\": \"question_1\", \"rule\": \"concordance\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"evidence\": {\"nbr_drop\": 0.029185, \"ndvi_drop\": 0.007671}, \"predicted_class\": \"no_change\"}, \"class_1_payload\": {\"concordance\": \"no_change\", \"evidence\": {\"nbr_drop\": 0.029185, \"ndvi_drop\": 0.007671, \"vh_delta_db\": -0.17847}, \"predicted_class\": \"no_change\", \"rule\": \"concordance\", \"thresholds\": {\"optical_change_ndvi_drop_gt\": 0.1, \"sar_change_vh_delta_db_lt\": -1.0}}, \"class_1_payload_keys\": [\"rule\", \"concordance\", \"predicted_class\", \"evidence\", \"thresholds\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 223, "id": "deforestation/000708/cross_modal_concordance", "dataset_id": "deforestation/000708/cross_modal_concordance", "sample_id": "000708", "question_type": "cross_modal_concordance", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e4e85d61a5865a99d76607b3ec23c67f8543156ab13218f1c9ae9511ef4d74b7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_change\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"vh_delta_db\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"vh_delta_db\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.17847}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"vh_delta_db\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.17847}, \"stats_1_payload\": {\"canopy_class\": \"unknown\", \"loss_area_ha\": null, \"loss_year_histogram_ha\": {}, \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.17847, \"treecover2000_mean_pct\": null}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"computed statistics `stats_1`\", \"tool\": \"ComputeSta...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `forest_disturbance` on `vh_delta_severity` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"hazard\":\"forest_disturbance\",\"scale\":\"vh_delta_severity\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.17847, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"units\": \"dB\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.17847}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c48e870b5384]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"forest_disturbance\", \"question\": \"question_1\", \"scale\": \"vh_delta_severity\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.17847, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"units\": \"dB\"}, \"score_1_payload\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.17847, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"severity_contract\": \"vh_delta_question_thresholds_v1\", \"severity_metric\": \"post_mean_db_minus_pre_mean_db\", \"thresholds_db\": {\"mild\": \"-1 <= delta < 0\", \"moderate\": \"-3 <= delta < -1\", \"none\": \"delta >= 0\", \"severe\": \"delta < -3\"}, \"units\": \"dB\", \"vh_delta_db\": -0.17847}, \"score_1_payload_keys\": [\"hazard\", \"scale\", \"vh_delta_db\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 224, "id": "deforestation/000708/severity_proxy_numeric", "dataset_id": "deforestation/000708/severity_proxy_numeric", "sample_id": "000708", "question_type": "severity_proxy_numeric", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c48e870b5384eb7c5fb1334e3e0fc7478b60a4282635314a04392717d328f05b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2020.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"forest_loss_support\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"forest_loss_support\"}}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH_minus_VV\": {\"delta\": 0.102929, \"post\": -6.342062, \"pre\": -6.444991}, \"VV\": {\"delta_db\": -0.159508}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"low\", \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"classification_rules\": [\"no_strong_signal\"], \"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"hansen\": {\"alert_overlap_wi...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `forest_loss` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"positive\":\"deforestation\",\"negative\":\"no_deforestation\"},\"rule\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2020-04-02 and 2020-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88a447b164f8]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"negative\": \"no_deforestation\", \"positive\": \"deforestation\"}, \"question\": \"question_1\", \"rule\": \"forest_loss\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.045037, \"rules_fired\": [\"no_strong_signal\"], \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"label\": \"no_deforestation\", \"predicted_class\": \"no_change\", \"rule\": \"forest_loss\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload_keys\": [\"rule\", \"label\", \"predicted_class\", \"confidence\", \"rules_fired\", \"false_positive_flags\", \"evidence\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_an...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 233, "id": "deforestation/000737/forest_loss_detection", "dataset_id": "deforestation/000737/forest_loss_detection", "sample_id": "000737", "question_type": "forest_loss_detection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "88a447b164f8511036eaf59276a41a2c30043f073ae219e789b082a982a145ae"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2020.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\",\"needs\":[\"question_1\",\"forest_mask_1\",\"optical_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\", \\\"optical\\\": \\\"scene_2\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\",\"optical\":\"scene_2\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\", \"optical\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `forest_loss_patches` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"forest_loss_patches\",\"filters\":{\"event_year\":\"2020\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2020, \"largest_patch_pixels\": 0, \"mask_kind\": \"hansen_lossyear_code_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"event_year\": \"2020\"}, \"question\": \"question_1\", \"target\": \"forest_loss_patches\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2020, \"largest_patch_pixels\": 0, \"mask_kind\": \"hansen_lossyear_code_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}, \"objects_1_payload\": {\"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"event_year\": 2020, \"fallback_used\": true, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2020, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": \"eve...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The target `forest_loss` is available in the work context; measure area in `hectares`.\",\"action\":{\"tool\":\"MeasureArea\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"target\":\"forest_loss\",\"unit\":\"hectares\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"measured area `area_1`\"\n- latest_handle = \"area_1\"\n- latest_tool = \"MeasureArea\"\n- latest_tool_ok = true\n- area_1_evidence = {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2020, \"largest_patch_pixels\": 0, \"mask_kind\": \"hansen_lossyear_code_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"forest_loss\", \"unit\": \"hectares\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"area_1_answer_ref\": \"ANSWER_JSON:area_1\", \"area_1_evidence\": {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}, \"area_1_payload\": {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"input_object_summary\": {\"component_count\": 0, \"largest_patch_pixels\": 0, \"target\": \"forest_loss_patches\"}, \"source\": {\"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"event_year\": 2020, \"fallback_used\": true, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2020, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": \"event_year_mask_has_no_valid_pixels\", \"fallback_used\": true, \"largest_patch_pixels\": 0, \"loss_pixel_count\": 0, \"mask_kind\": \"hansen_lossyear_code_mask\", \"mask_path\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_hansen\\\", \\\"criterion\\\": \\\"validation\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_hansen\",\"criterion\":\"validation\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2020. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- area_1_evidence = {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2020, \"largest_patch_pixels\": 0, \"mask_kind\": \"hansen_lossyear_code_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=80fdbcf9553b]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}}, \"compare_1_payload\": {\"classification_rules\": [], \"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"hansen\": {\"alert_overlap_with_change_region\": null, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2020, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": \"event_year_mask_has_no_valid_pixels\", \"fallback_used\": true, \"largest_pat...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 234, "id": "deforestation/000737/area_estimation", "dataset_id": "deforestation/000737/area_estimation", "sample_id": "000737", "question_type": "area_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "MeasureArea", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "80fdbcf9553bf90a5017eff60087f0c4a254d9d9420fab4f33895e619b978558"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2020.tif, lossyear.tif\n- optical_timeseries_1 [optical_timeseries] - Sentinel-2 temporal NDVI/NBR series across pre/post epochs.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - Sentinel-1 VH/VV time series across pre/post epochs.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_timeseries_1\",\"sar_timeseries_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_timeseries_1\",\"role\":\"ndvi_nbr\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_timeseries_1\", \"question\": \"question_1\", \"role\": \"ndvi_nbr\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_timeseries_1\",\"role\":\"vh\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_timeseries_1\", \"question\": \"question_1\", \"role\": \"vh\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_timeseries` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_timeseries\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_timeseries\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `dominant_clearing_interval` over `pre_post_epochs`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"change\":\"dominant_clearing_interval\",\"window\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_evidence = {\"confidence\": \"none\", \"drop_magnitude\": 0.046976, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"dominant_clearing_interval\", \"question\": \"question_1\", \"window\": \"pre_post_epochs\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_evidence\": {\"confidence\": \"none\", \"drop_magnitude\": 0.046976, \"signal_used\": \"multi\"}, \"change_1_payload\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 0.0, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": false, \"confidence\": \"none\", \"drop_magnitude\": 0.046976, \"epoch_series\": [{\"NBR\": 0.668006, \"NDVI\": 0.887827, \"epoch\": \"pre_3\"}, {\"NBR\": 0.673092, \"NDVI\": 0.90388, \"epoch\": \"pre_2\"}, {\"NBR\": 0.653658, \"NDVI\": 0.866621, \"epoch\": \"post_1\"}, {\"NBR\": 0.662578, \"NDVI\": 0.857274, \"epoch\": \"post_2\"}, {\"NBR\": 0.650643, \"NDVI\": 0.828556, \"epoch\": \"post_...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `change_window` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"change_1\",\"labels\":{\"none\":\"null\",\"changed\":\"start_epoch_to_end_epoch\"},\"rule\":\"change_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"none\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"predicted_class\": \"no_deforestation\", \"source\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"min...\n- class_1_summary = \"classified change `class_1`\"\n- change_1_evidence = {\"confidence\": \"none\", \"drop_magnitude\": 0.046976, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=87d4561797ad]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"changed\": \"start_epoch_to_end_epoch\", \"none\": \"null\"}, \"question\": \"question_1\", \"rule\": \"change_window\", \"work\": \"change_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"none\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"predicted_class\": \"no_deforestation\", \"source\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 0.0, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": false, \"confidence\": \"none\", \"drop_magnitude\": 0.046976, \"epoch_series\": [{\"NBR\": 0.668006, \"NDVI\": 0.887827, \"epoch\": \"pre_3\"}, {\"NBR\": 0.673092, \"NDVI\": 0.90388, \"epoch\": \"pre_2\"}, {\"NBR\": 0.653658, \"NDVI\": 0.866621, \"epoch\": \"post_1\"}, {\"NBR\": 0.6625...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 235, "id": "deforestation/000737/change_window_estimation", "dataset_id": "deforestation/000737/change_window_estimation", "sample_id": "000737", "question_type": "change_window_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "DetectChange", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "87d4561797ad818f8acb368ca6ba8b2b1d199703a47f1b23895991447295ce44"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2020.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_false_positive` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_false_positive\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_false_positive\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"cause_attribution\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"cause_attribution\"}}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH_minus_VV\": {\"delta\": 0.102929, \"post\": -6.342062, \"pre\": -6.444991}, \"VV\": {\"delta_db\": -0.159508}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"low\", \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"classification_rules\": [\"no_strong_signal\"], \"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"hansen\": {\"alert_overlap_with_c...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `false_positive_type` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"deforestation\":true,\"plantation_harvest\":true,\"water_flood_edge\":true,\"burn_bare_soil\":true,\"agriculture\":true,\"no_change\":true},\"rule\":\"false_positive_type\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 99.614456}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=045353b306d2]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"agriculture\": true, \"burn_bare_soil\": true, \"deforestation\": true, \"no_change\": true, \"plantation_harvest\": true, \"water_flood_edge\": true}, \"question\": \"question_1\", \"rule\": \"false_positive_type\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037, \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload\": {\"cause\": \"no_change\", \"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037, \"rules_fired\": [\"no_strong_signal\"], \"sar_vh_delta_db\": -0.056578}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rule\": \"false_positive_type\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload_keys\": [\"rule\", \"cause\", \"predicted_c...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 236, "id": "deforestation/000737/false_positive_rejection", "dataset_id": "deforestation/000737/false_positive_rejection", "sample_id": "000737", "question_type": "false_positive_rejection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "045353b306d2bdc0ff3bfc823179b3fc14dbcccd5d46462feaafbeb98a37bc6b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover2000 raster for the AOI.; refs: treecover2000.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\"} into a `baseline_canopy` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\"},\"mode\":\"baseline_canopy\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\"}, \"mode\": \"baseline_canopy\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"treecover_mean_pct\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"treecover_mean_pct\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"treecover2000_mean_pct\": 99.614456}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"treecover_mean_pct\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"treecover2000_mean_pct\": 99.614456}, \"stats_1_payload\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"loss_year_histogram_ha\": {}, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"sar_vh_delta_db\": null, \"treecover2000_mean_pct\": 99.614456}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": [...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `canopy_density` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"labels\":{\"dense_forest\":\">60\",\"open_canopy\":\"30<=mean<=60\",\"sparse\":\"<30\"},\"rule\":\"canopy_density\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 99.614456}\n- class_1_summary = \"classified change `class_1`\"\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"treecover2000_mean_pct\": 99.614456}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ccffe2707c34]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"dense_forest\": \">60\", \"open_canopy\": \"30<=mean<=60\", \"sparse\": \"<30\"}, \"question\": \"question_1\", \"rule\": \"canopy_density\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"predicted_class\": \"dense_forest\", \"treecover2000_mean_pct\": 99.614456}, \"class_1_payload\": {\"predicted_class\": \"dense_forest\", \"rule\": \"canopy_density\", \"treecover2000_mean_pct\": 99.614456}, \"class_1_payload_keys\": [\"rule\", \"treecover2000_mean_pct\", \"predicted_class\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified change `class_1`\", \"tool\": \"ClassifyChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 237, "id": "deforestation/000737/baseline_canopy_assessment", "dataset_id": "deforestation/000737/baseline_canopy_assessment", "sample_id": "000737", "question_type": "baseline_canopy_assessment", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ccffe2707c343143596a56f66f36eb624f573e543b40bb01f0cc4bf8d925ac73"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\nREGISTERED INPUTS\n- forest_loss_history_1 [forest_loss_history] - Full Hansen lossyear raster for 2001-2023 attribution.; refs: lossyear.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_loss_history_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_loss_history_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_loss_history_1\",\"role\":\"hansen\",\"time\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_loss_history_1\", \"question\": \"question_1\", \"role\": \"hansen\", \"time\": \"2001_2023\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"loss_history\\\": \\\"scene_1\\\"} into a `forest_loss_history` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"loss_history\":\"scene_1\"},\"mode\":\"forest_loss_history\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"loss_history\": \"scene_1\"}, \"mode\": \"forest_loss_history\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"loss_area_ha_by_year\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"loss_area_ha_by_year\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"treecover2000_mean_pct\": 99.614456}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"loss_area_ha_by_year\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"treecover2000_mean_pct\": 99.614456}, \"stats_1_payload\": {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"loss_year_histogram_ha\": {}, \"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"sar_vh_delta_db\": null, \"treecover2000_mean_pct\": 99.614456}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `sta...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `largest_loss_year` over `2001_2023`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"change\":\"largest_loss_year\",\"window\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_summary = \"detected change `change_1`\"\n- stats_1_evidence = {\"canopy_class\": \"dense_forest\", \"loss_area_ha\": 0.0, \"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"treecover2000_mean_pct\": 99.614456}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c1dbe56478e]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"largest_loss_year\", \"question\": \"question_1\", \"window\": \"2001_2023\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_payload\": {\"change\": \"largest_loss_year\", \"loss_area_ha_by_year\": {}, \"loss_ha\": 0.0, \"window\": \"2001_2023\", \"year\": \"no_loss\"}, \"change_1_payload_keys\": [\"change\", \"window\", \"year\", \"loss_ha\", \"loss_area_ha_by_year\"], \"change_1_summary\": \"detected change `change_1`\", \"latest_answer_ref\": \"ANSWER_JSON:change_1\", \"latest_handle\": \"change_1\", \"latest_summary\": \"detected change `change_1`\", \"latest_tool\": \"DetectChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"detected change `change_1`\", \"tool\": \"DetectChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 238, "id": "deforestation/000737/multi_year_loss_attribution", "dataset_id": "deforestation/000737/multi_year_loss_attribution", "sample_id": "000737", "question_type": "multi_year_loss_attribution", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9c1dbe56478e5f59f3d242d2bd2c40c7bc9f65c47614dbd2334ebaec25e18e25"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\nREGISTERED INPUTS\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\"} into a `forest_cross_modal` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\"},\"mode\":\"forest_cross_modal\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_cross_modal\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_vh\\\", \\\"criterion\\\": \\\"concordance\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_vh\",\"criterion\":\"concordance\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH_minus_VV\": {\"delta\": 0.102929, \"post\": -6.342062, \"pre\": -6.444991}, \"VV\": {\"delta_db\": -0.159508}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7befdaaeef75]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH_minus_VV\": {\"delta\": 0.102929, \"post\": -6.342062, \"pre\": -6.444991}, \"VV\": {\"delta_db\": -0.159508}}}, \"compare_1_payload\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"concordance\": \"no_change\", \"evidence\": {\"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037, \"vh_delta_db\": -0.056578}, \"optical\": {\"NBR\": {\"drop\": 0.014923, \"per_epoch_mean\": {\"post_1\": 0.653658, \"post_2\": 0.662578, \"post_3\": 0.650643, \"pre_2\": 0.673092, \"pre_3\": 0.668006}, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"per_epoch...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `concordance` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"concordant\":true,\"discordant\":true,\"no_change\":true},\"rule\":\"concordance\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"evidence\": {\"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037}, \"predicted_class\": \"no_change\"}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.014923, \"post_mean\": 0.655626, \"pre_mean\": 0.670549}, \"NDVI\": {\"drop\": 0.045037, \"post_mean\": 0.850817, \"pre_mean\": 0.895853}}, \"sar\": {\"VH\": {\"delta_db\": -0.056578}, \"VH_minus_VV\": {\"delta\": 0.102929, \"post\": -6.342062, \"pre\": -6.444991}, \"VV\": {\"delta_db\": -0.159508}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7befdaaeef75]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"concordant\": true, \"discordant\": true, \"no_change\": true}, \"question\": \"question_1\", \"rule\": \"concordance\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"evidence\": {\"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037}, \"predicted_class\": \"no_change\"}, \"class_1_payload\": {\"concordance\": \"no_change\", \"evidence\": {\"nbr_drop\": 0.014923, \"ndvi_drop\": 0.045037, \"vh_delta_db\": -0.056578}, \"predicted_class\": \"no_change\", \"rule\": \"concordance\", \"thresholds\": {\"optical_change_ndvi_drop_gt\": 0.1, \"sar_change_vh_delta_db_lt\": -1.0}}, \"class_1_payload_keys\": [\"rule\", \"concordance\", \"predicted_class\", \"evidence\", \"thresholds\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 239, "id": "deforestation/000737/cross_modal_concordance", "dataset_id": "deforestation/000737/cross_modal_concordance", "sample_id": "000737", "question_type": "cross_modal_concordance", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7befdaaeef756472e1cba105a2f13991e5de80a9123f49c39d118e6fb02dda02"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_change\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"vh_delta_db\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"vh_delta_db\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.056578}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"vh_delta_db\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.056578}, \"stats_1_payload\": {\"canopy_class\": \"unknown\", \"loss_area_ha\": null, \"loss_year_histogram_ha\": {}, \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.056578, \"treecover2000_mean_pct\": null}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"computed statistics `stats_1`\", \"tool\": \"ComputeS...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `forest_disturbance` on `vh_delta_severity` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"hazard\":\"forest_disturbance\",\"scale\":\"vh_delta_severity\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.056578, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"units\": \"dB\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -0.056578}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=02f6230a497b]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"forest_disturbance\", \"question\": \"question_1\", \"scale\": \"vh_delta_severity\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.056578, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"units\": \"dB\"}, \"score_1_payload\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -0.056578, \"scale\": \"vh_delta_severity\", \"severity\": \"mild\", \"severity_contract\": \"vh_delta_question_thresholds_v1\", \"severity_metric\": \"post_mean_db_minus_pre_mean_db\", \"thresholds_db\": {\"mild\": \"-1 <= delta < 0\", \"moderate\": \"-3 <= delta < -1\", \"none\": \"delta >= 0\", \"severe\": \"delta < -3\"}, \"units\": \"dB\", \"vh_delta_db\": -0.056578}, \"score_1_payload_keys\": [\"hazard\", \"scale\", \"vh_delta_...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 240, "id": "deforestation/000737/severity_proxy_numeric", "dataset_id": "deforestation/000737/severity_proxy_numeric", "sample_id": "000737", "question_type": "severity_proxy_numeric", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "02f6230a497b7bfcf82cfedcd4f3a02c7d888d8de5d241cd458faeffc1121852"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2019.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"forest_loss_support\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"forest_loss_support\"}}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_mi...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_minus_VV\": {\"delta\": -0.700398, \"post\": -7.077617, \"pre\": -6.37722}, \"VV\": {\"delta_db\": -0.877472}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"low\", \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"classification_rules\": [\"no_strong_signal\"], \"compare\": {\"criterion\": \"forest_loss_support\", \"signals\": \"optical_sar_hansen\"}, \"hansen\": {\"alert_overlap_with_...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `forest_loss` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"positive\":\"deforestation\",\"negative\":\"no_deforestation\"},\"rule\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nLooking at the multi-temporal Sentinel-2 RGB panel for this AOI, decide whether real forest loss occurred between 2019-04-02 and 2019-09-29. Answer 'deforestation' or 'no_deforestation' and report the supporting NDVI drop, SAR VH change (dB), and Hansen loss area (ha).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / forest loss detection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / forest loss detection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / forest loss detection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_mi...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=af8399f73087]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"negative\": \"no_deforestation\", \"positive\": \"deforestation\"}, \"question\": \"question_1\", \"rule\": \"forest_loss\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.030427, \"rules_fired\": [\"no_strong_signal\"], \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"label\": \"no_deforestation\", \"predicted_class\": \"no_change\", \"rule\": \"forest_loss\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload_keys\": [\"rule\", \"label\", \"predicted_class\", \"confidence\", \"rules_fired\", \"false_positive_flags\", \"evidence\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answ...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 345, "id": "deforestation/001109/forest_loss_detection", "dataset_id": "deforestation/001109/forest_loss_detection", "sample_id": "001109", "question_type": "forest_loss_detection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "af8399f730872efc8c16063bb0cb5edf32fd7f41f5b7c9d36554d5c10d0415d4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2019.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\",\"needs\":[\"question_1\",\"forest_mask_1\",\"optical_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\", \\\"optical\\\": \\\"scene_2\\\"} into a `forest_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\",\"optical\":\"scene_2\"},\"mode\":\"forest_change\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\", \"optical\": \"scene_2\"}, \"mode\": \"forest_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `forest_loss_patches` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"forest_loss_patches\",\"filters\":{\"event_year\":\"2019\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2019, \"largest_patch_pixels\": 0, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"event_year\": \"2019\"}, \"question\": \"question_1\", \"target\": \"forest_loss_patches\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2019, \"largest_patch_pixels\": 0, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}, \"objects_1_payload\": {\"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"event_year\": 2019, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2019, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": null,...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The target `forest_loss` is available in the work context; measure area in `hectares`.\",\"action\":{\"tool\":\"MeasureArea\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"target\":\"forest_loss\",\"unit\":\"hectares\"}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"measured area `area_1`\"\n- latest_handle = \"area_1\"\n- latest_tool = \"MeasureArea\"\n- latest_tool_ok = true\n- area_1_evidence = {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2019, \"largest_patch_pixels\": 0, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"forest_loss\", \"unit\": \"hectares\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"area_1_answer_ref\": \"ANSWER_JSON:area_1\", \"area_1_evidence\": {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}, \"area_1_payload\": {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"input_object_summary\": {\"component_count\": 0, \"largest_patch_pixels\": 0, \"target\": \"forest_loss_patches\"}, \"source\": {\"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"event_year\": 2019, \"fallback_used\": false, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2019, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 0, \"loss_pixel_count\": 0, \"mask_kind\": \"event_year_binary_mask\", \"mask_path\": \"masks/loss_year_2019.tif\", \"mask...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_hansen\\\", \\\"criterion\\\": \\\"validation\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_hansen\",\"criterion\":\"validation\"}}}}"}, {"role": "user", "content": "QUESTION\nEstimate the total deforested area in hectares inside this AOI for event year 2019. Use the Hansen GFC 2025 v1.13 loss-year mask, then validate the signal with NDVI/NBR drops in the same region.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / area estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\", \"optical_stack_1\"], \"objective\": \"deterministic deforestation / area estimation: prepare forest-change evidence from forest_mask_1, optical_stack_1. Verify the local stacks/masks, isolate the relevant disturbance object, and report measured area support with a reusable work handle.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / area estimation: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- area_1_evidence = {\"area\": 0.0, \"area_ha\": 0.0, \"area_m2\": 0.0, \"target\": \"forest_loss\", \"unit\": \"hectares\"}\n- area_1_summary = \"measured area `area_1`\"\n- objects_1_evidence = {\"area_m2\": 0.0, \"component_count\": 0, \"event_year\": 2019, \"largest_patch_pixels\": 0, \"mask_kind\": \"event_year_binary_mask\", \"pixel_count\": 0, \"target\": \"forest_loss_patches\", \"total_area_ha\": 0.0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=064117c8f688]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}}, \"compare_1_payload\": {\"classification_rules\": [], \"compare\": {\"criterion\": \"validation\", \"signals\": \"ndvi_nbr_vs_hansen\"}, \"hansen\": {\"alert_overlap_with_change_region\": null, \"generated_quality_report\": {\"contains_expected_answer\": false, \"event_year\": 2019, \"generated_from_declared_inputs\": true, \"loss_event_year\": {\"area_ha\": 0.0, \"area_m2\": 0.0, \"component_count\": 0, \"crs\": \"EPSG:4326\", \"fallback_reason\": null, \"fallback_used\": false, \"largest_patch_pixels\": 0, \"loss_pixel_count\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 346, "id": "deforestation/001109/area_estimation", "dataset_id": "deforestation/001109/area_estimation", "sample_id": "001109", "question_type": "area_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "MeasureArea", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "064117c8f68887ea01e1fe352e4a3f5daeb1c0e6dcd6a77169ea1e2e588cdd78"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2019.tif, lossyear.tif\n- optical_timeseries_1 [optical_timeseries] - Sentinel-2 temporal NDVI/NBR series across pre/post epochs.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - Sentinel-1 VH/VV time series across pre/post epochs.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_timeseries_1\",\"sar_timeseries_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_timeseries_1\",\"role\":\"ndvi_nbr\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_timeseries_1\", \"question\": \"question_1\", \"role\": \"ndvi_nbr\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_timeseries_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_timeseries_1\",\"role\":\"vh\",\"time\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_timeseries_1\", \"question\": \"question_1\", \"role\": \"vh\", \"time\": \"pre_post_epochs\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_timeseries` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_timeseries\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_timeseries\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `dominant_clearing_interval` over `pre_post_epochs`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"change\":\"dominant_clearing_interval\",\"window\":\"pre_post_epochs\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_evidence = {\"confidence\": \"none\", \"drop_magnitude\": 0.192234, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"dominant_clearing_interval\", \"question\": \"question_1\", \"window\": \"pre_post_epochs\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_answer_ref\": \"ANSWER_JSON:change_1\", \"change_1_evidence\": {\"confidence\": \"none\", \"drop_magnitude\": 0.192234, \"signal_used\": \"multi\"}, \"change_1_payload\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 0.0, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": false, \"confidence\": \"none\", \"drop_magnitude\": 0.192234, \"epoch_series\": [{\"NBR\": 0.134484, \"NDVI\": 0.473222, \"epoch\": \"pre_3\"}, {\"NBR\": 0.03983, \"NDVI\": 0.422776, \"epoch\": \"pre_2\"}, {\"NBR\": 0.033958, \"NDVI\": 0.37664, \"epoch\": \"pre_1\"}, {\"NBR\": 0.161269, \"NDVI\": 0.494902, \"epoch\": \"post_1\"}, {\"NBR\": 0.058393, \"NDVI\": 0.402416, \"epoch\": \"post_2\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `change_window` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"change_1\",\"labels\":{\"none\":\"null\",\"changed\":\"start_epoch_to_end_epoch\"},\"rule\":\"change_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing AOI-wide means for the available Sentinel-2 epochs in canonical order (pre_3, pre_2, pre_1, post_1, post_2, post_3), identify the dominant adjacent clearing interval by maximizing NDVI_drop + 0.5*NBR_drop; skip any undeclared epoch. Return label deforestation and that interval only when Hansen event-year loss is at least 0.5 ha; otherwise return label no_deforestation and a null interval. Report Hansen loss area; the whole-period NDVI drop within positive event-year loss pixels, falling back to AOI-wide valid pixels when none exist; Sentinel-1 VH delta dB; and the selected-window NDVI drop, NBR drop, and composite score (the selected-window fields are null when the interval is null).\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / change window estimation task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_timeseries_1\", \"sar_timeseries_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / change window estimation: prepare reusable evidence from optical_timeseries_1, sar_timeseries_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / change window estimation: identify the temporal change signal and convert it into the requested state/window label. Report the interval or label with numeric support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_timeseries_1 [optical_timeseries] - optical_timeseries_1: Sentinel-2 temporal NDVI/NBR series across pre/post epochs.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_timeseries_1 [sar_timeseries] - sar_timeseries_1: Sentinel-1 VH/VV time series across pre/post epochs.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"none\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"predicted_class\": \"no_deforestation\", \"source\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"mini...\n- class_1_summary = \"classified change `class_1`\"\n- change_1_evidence = {\"confidence\": \"none\", \"drop_magnitude\": 0.192234, \"signal_used\": \"multi\"}\n- change_1_summary = \"detected change `change_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5670ba01b74e]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"changed\": \"start_epoch_to_end_epoch\", \"none\": \"null\"}, \"question\": \"question_1\", \"rule\": \"change_window\", \"work\": \"change_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"none\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"predicted_class\": \"no_deforestation\", \"source\": {\"best_window\": null, \"change\": \"dominant_clearing_interval\", \"change_window_contract\": {\"confirmation_signal\": \"hansen_event_year_loss_area_ha\", \"minimum_confirmed_loss_ha\": 0.5, \"null_when_unconfirmed\": true, \"observed_loss_area_ha\": 0.0, \"version\": \"loss_confirmed_temporal_window_v1\"}, \"clearing_visible\": false, \"confidence\": \"none\", \"drop_magnitude\": 0.192234, \"epoch_series\": [{\"NBR\": 0.134484, \"NDVI\": 0.473222, \"epoch\": \"pre_3\"}, {\"NBR\": 0.03983, \"NDVI\": 0.422776, \"epoch\": \"pre_2\"}, {\"NBR\": 0.033958, \"NDVI\": 0.37664, \"epoch\": \"pre_1\"}, {\"NBR\": 0.161269,...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 347, "id": "deforestation/001109/change_window_estimation", "dataset_id": "deforestation/001109/change_window_estimation", "sample_id": "001109", "question_type": "change_window_estimation", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "DetectChange", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5670ba01b74e7db2c8c51095baea0689cf64e4cf6e1f2d6d7383ffb9da2d00af"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover/loss masks for the AOI.; refs: loss_year_2019.tif, lossyear.tif\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\", \\\"forest\\\": \\\"scene_3\\\"} into a `forest_false_positive` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\",\"forest\":\"scene_3\"},\"mode\":\"forest_false_positive\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_3\", \"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_false_positive\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"optical_sar_hansen\\\", \\\"criterion\\\": \\\"cause_attribution\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"optical_sar_hansen\",\"criterion\":\"cause_attribution\"}}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_mi...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_minus_VV\": {\"delta\": -0.700398, \"post\": -7.077617, \"pre\": -6.37722}, \"VV\": {\"delta_db\": -0.877472}}}, \"compare_1_payload\": {\"classification\": {\"confidence\": \"low\", \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"classification_rules\": [\"no_strong_signal\"], \"compare\": {\"criterion\": \"cause_attribution\", \"signals\": \"optical_sar_hansen\"}, \"hansen\": {\"alert_overlap_with_chan...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `false_positive_type` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"deforestation\":true,\"plantation_harvest\":true,\"water_flood_edge\":true,\"burn_bare_soil\":true,\"agriculture\":true,\"no_change\":true},\"rule\":\"false_positive_type\"}}}"}, {"role": "user", "content": "QUESTION\nApply the declared ordered remote-sensing proxy rules to classify the change pattern in this AOI. Return exactly one operational cause class from: deforestation, plantation_harvest, water_flood_edge, burn_bare_soil, agriculture, no_change. These are benchmark proxy classes, not independently field-verified causal labels. Report the optical NDVI/NBR drops, Sentinel-1 VH delta dB and post-event VV mean dB, Hansen loss area, baseline tree-cover mean percentage, and ordered rules fired.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / false positive rejection task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / false positive rejection: prepare reusable evidence from optical_stack_1, sar_stack_1, forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / false positive rejection: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover/loss masks for the AOI.\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"classification_rules\": [\"no_strong_signal\"], \"hansen\": {\"loss_area_ha\": 0.0, \"treecover2000_mean_pct\": 2.13652}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_mi...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=98ef665fea1b]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"agriculture\": true, \"burn_bare_soil\": true, \"deforestation\": true, \"no_change\": true, \"plantation_harvest\": true, \"water_flood_edge\": true}, \"question\": \"question_1\", \"rule\": \"false_positive_type\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427, \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload\": {\"cause\": \"no_change\", \"confidence\": \"low\", \"evidence\": {\"hansen_loss_area_ha\": 0.0, \"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427, \"rules_fired\": [\"no_strong_signal\"], \"sar_vh_delta_db\": -1.57787}, \"false_positive_flags\": [], \"predicted_class\": \"no_change\", \"rule\": \"false_positive_type\", \"rules_fired\": [\"no_strong_signal\"]}, \"class_1_payload_keys\": [\"rule\", \"cause\", \"predicted_cla...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 348, "id": "deforestation/001109/false_positive_rejection", "dataset_id": "deforestation/001109/false_positive_rejection", "sample_id": "001109", "question_type": "false_positive_rejection", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "98ef665fea1b2982d8c7c09cc89f7bff3206a123002f25a24e17109c6be4e5e7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\nREGISTERED INPUTS\n- forest_mask_1 [forest_mask] - Hansen treecover2000 raster for the AOI.; refs: treecover2000.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_mask_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_mask_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_mask_1\",\"role\":\"hansen\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_mask_1\", \"question\": \"question_1\", \"role\": \"hansen\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"forest\\\": \\\"scene_1\\\"} into a `baseline_canopy` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"forest\":\"scene_1\"},\"mode\":\"baseline_canopy\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"forest\": \"scene_1\"}, \"mode\": \"baseline_canopy\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"treecover_mean_pct\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"treecover_mean_pct\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"sparse\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}], \"treecover2000_mean_pct\": 2.13652}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"treecover_mean_pct\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"sparse\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}], \"treecover2000_mean_pct\": 2.13652}, \"stats_1_payload\": {\"canopy_class\": \"sparse\", \"loss_area_ha\": 0.0, \"loss_year_histogram_ha\": {\"2012\": 0.081167, \"2013\": 0.486993}, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"sar_vh_delta_db\": null, \"treecover2000_mean_pct\": 2.13652}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `canopy_density` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"labels\":{\"dense_forest\":\">60\",\"open_canopy\":\"30<=mean<=60\",\"sparse\":\"<30\"},\"rule\":\"canopy_density\"}}}"}, {"role": "user", "content": "QUESTION\nCompute the arithmetic mean Hansen treecover2000 percentage over all valid pixels in the packaged AOI raster (exclude non-finite values and declared nodata 255; treat zero as valid). Return dense_forest when the mean is >60%, open_canopy when 30% <= the mean <= 60%, or sparse when the mean is <30%. Report both the unrounded mean and the class.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / baseline canopy assessment task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_mask_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: prepare reusable evidence from forest_mask_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / baseline canopy assessment: classify the requested state from prepared evidence using the stated labels/rules. Report the class with support values.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_mask_1 [forest_mask] - forest_mask_1: Hansen treecover2000 raster for the AOI.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"predicted_class\": \"sparse\", \"treecover2000_mean_pct\": 2.13652}\n- class_1_summary = \"classified change `class_1`\"\n- stats_1_evidence = {\"canopy_class\": \"sparse\", \"loss_area_ha\": 0.0, \"metrics\": {\"canopy_cover\": true, \"treecover_mean_pct\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}], \"treecover2000_mean_pct\": 2.13652}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=337fd1ed4f05]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"dense_forest\": \">60\", \"open_canopy\": \"30<=mean<=60\", \"sparse\": \"<30\"}, \"question\": \"question_1\", \"rule\": \"canopy_density\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"predicted_class\": \"sparse\", \"treecover2000_mean_pct\": 2.13652}, \"class_1_payload\": {\"predicted_class\": \"sparse\", \"rule\": \"canopy_density\", \"treecover2000_mean_pct\": 2.13652}, \"class_1_payload_keys\": [\"rule\", \"treecover2000_mean_pct\", \"predicted_class\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classified change `class_1`\", \"tool\": \"ClassifyChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 349, "id": "deforestation/001109/baseline_canopy_assessment", "dataset_id": "deforestation/001109/baseline_canopy_assessment", "sample_id": "001109", "question_type": "baseline_canopy_assessment", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "337fd1ed4f0551bcd3b4f496a4592debea40c3c7ab527189f5afbd3ef99b0629"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\nREGISTERED INPUTS\n- forest_loss_history_1 [forest_loss_history] - Full Hansen lossyear raster for 2001-2023 attribution.; refs: lossyear.tif\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"forest_loss_history_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `forest_loss_history_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"forest_loss_history_1\",\"role\":\"hansen\",\"time\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"forest_loss_history_1\", \"question\": \"question_1\", \"role\": \"hansen\", \"time\": \"2001_2023\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"loss_history\\\": \\\"scene_1\\\"} into a `forest_loss_history` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"loss_history\":\"scene_1\"},\"mode\":\"forest_loss_history\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"loss_history\": \"scene_1\"}, \"mode\": \"forest_loss_history\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"loss_area_ha_by_year\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"loss_area_ha_by_year\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"loss_area_ha_by_year\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_evidence\": {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}]}, \"stats_1_payload\": {\"analysis_window\": {\"end_year\": 2023, \"start_year\": 2001}, \"loss_year_histogram_ha\": {\"2012\": 0.081167, \"2013\": 0.486993}, \"measurement_contract\": \"hansen_grid_area_by_year_v1\", \"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"valid_pixel_count\": 32508}, \"stats_1_payload_keys\": [\"metrics\", \"loss_year_histogram_ha\", \"valid_pixel_count\", \"analysis_window\", \"measurement_contract\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"com...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Change evidence is required; detect `largest_loss_year` over `2001_2023`.\",\"action\":{\"tool\":\"DetectChange\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"change\":\"largest_loss_year\",\"window\":\"2001_2023\"}}}"}, {"role": "user", "content": "QUESTION\nUsing only Hansen lossyear codes 1 through 23 (calendar years 2001 through 2023) inside this AOI, compute each year's grid-aware loss area in hectares and return the year with the largest area plus that six-decimal area. Break an equal six-decimal area tie in favor of the earliest year. If no valid pixel has a code from 1 through 23, return year 'no_loss' and loss_ha 0.0. Code 0 is valid no-loss and nodata 255 is excluded.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / multi year loss attribution task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"forest_loss_history_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: prepare reusable evidence from forest_loss_history_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / multi year loss attribution: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- forest_loss_history_1 [forest_loss_history] - forest_loss_history_1: Full Hansen lossyear raster for 2001-2023 attribution.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"detected change `change_1`\"\n- latest_handle = \"change_1\"\n- latest_tool = \"DetectChange\"\n- latest_tool_ok = true\n- change_1_summary = \"detected change `change_1`\"\n- stats_1_evidence = {\"metrics\": {\"forest_loss_history\": true, \"loss_area_ha_by_year\": true}, \"top_loss_years_ha\": [{\"area_ha\": 0.487, \"year\": \"2013\"}, {\"area_ha\": 0.081, \"year\": \"2012\"}]}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7bc6b1026af8]\n\nLAST OBSERVATION\n{\"args\": {\"change\": \"largest_loss_year\", \"question\": \"question_1\", \"window\": \"2001_2023\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"change_1_payload\": {\"change\": \"largest_loss_year\", \"loss_area_ha_by_year\": {\"2012\": 0.081167, \"2013\": 0.486993}, \"loss_ha\": 0.486993, \"measurement_contract\": \"hansen_grid_area_by_year_v1\", \"tie_break_rule\": \"earliest_year\", \"window\": \"2001_2023\", \"year\": 2013}, \"change_1_payload_keys\": [\"change\", \"window\", \"year\", \"loss_ha\", \"loss_area_ha_by_year\", \"tie_break_rule\", \"measurement_contract\"], \"change_1_summary\": \"detected change `change_1`\", \"latest_handle\": \"change_1\", \"latest_summary\": \"detected change `change_1`\", \"latest_tool\": \"DetectChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"detected change `change_1`\", \"tool\": \"DetectChange\"}\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 350, "id": "deforestation/001109/multi_year_loss_attribution", "dataset_id": "deforestation/001109/multi_year_loss_attribution", "sample_id": "001109", "question_type": "multi_year_loss_attribution", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7bc6b1026af8e180a4b950eeeaf5531f1aec187280589a9ec8058be8e0cf785d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\nREGISTERED INPUTS\n- optical_stack_1 [optical_stack] - Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.; refs: pre_3.tif, pre_2.tif, pre_3.png, pre_2.png\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"optical_stack_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `optical_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"optical_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"optical_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"optical\\\": \\\"scene_1\\\", \\\"sar\\\": \\\"scene_2\\\"} into a `forest_cross_modal` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"optical\":\"scene_1\",\"sar\":\"scene_2\"},\"mode\":\"forest_cross_modal\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"optical\": \"scene_1\", \"sar\": \"scene_2\"}, \"mode\": \"forest_cross_modal\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"signals\\\": \\\"ndvi_nbr_vs_vh\\\", \\\"criterion\\\": \\\"concordance\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"signals\":\"ndvi_nbr_vs_vh\",\"criterion\":\"concordance\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_minus_VV\": {\"delta\": -0.700398, \"post\": -7.077617, \"pre\": -6.37722}, \"VV\": {\"delta_db\": -0.877472}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=47c4fef16f82]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_minus_VV\": {\"delta\": -0.700398, \"post\": -7.077617, \"pre\": -6.37722}, \"VV\": {\"delta_db\": -0.877472}}}, \"compare_1_payload\": {\"compare\": {\"criterion\": \"concordance\", \"signals\": \"ndvi_nbr_vs_vh\"}, \"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427, \"vh_delta_db\": -1.57787}, \"optical\": {\"NBR\": {\"drop\": 0.025977, \"per_epoch_mean\": {\"post_1\": 0.161269, \"post_2\": 0.058393, \"post_3\": -0.089322, \"pre_1\": 0.033958, \"pre_2\": 0.03983, \"pre_3\": 0.134484}, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Evidence is ready; apply rule `concordance` to the allowed labels for a grounded decision.\",\"action\":{\"tool\":\"ClassifyChange\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"labels\":{\"concordant\":true,\"discordant\":true,\"no_change\":true},\"rule\":\"concordance\"}}}"}, {"role": "user", "content": "QUESTION\nCompare whole-AOI optical and SAR signals using the available declared pre/post epochs. Define optical change as mean-pre minus mean-post NDVI drop > 0.10 and SAR change as mean-post minus mean-pre VH delta < -1.0 dB (equality at either threshold is no change for that modality). Return 'concordant' when both indicate change, 'discordant' when only one does, or 'no_change' when neither does. Report NDVI drop, contextual NBR drop, and VH delta dB.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / cross modal concordance task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"optical_stack_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: prepare reusable evidence from optical_stack_1, sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / cross modal concordance: compare the prepared sources and classify the state from their combined numeric support. Report the label and the evidence values behind it.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- optical_stack_1 [optical_stack] - optical_stack_1: Sentinel-2 pre/post stack and RGB panel for NDVI/NBR reasoning.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"classified change `class_1`\"\n- latest_handle = \"class_1\"\n- latest_tool = \"ClassifyChange\"\n- latest_tool_ok = true\n- class_1_evidence = {\"evidence\": {\"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427}, \"predicted_class\": \"discordant\"}\n- class_1_summary = \"classified change `class_1`\"\n- compare_1_evidence = {\"optical\": {\"NBR\": {\"drop\": 0.025977, \"post_mean\": 0.043447, \"pre_mean\": 0.069424}, \"NDVI\": {\"drop\": 0.030427, \"post_mean\": 0.393786, \"pre_mean\": 0.424213}}, \"sar\": {\"VH\": {\"delta_db\": -1.57787}, \"VH_minus_VV\": {\"delta\": -0.700398, \"post\": -7.077617, \"pre\": -6.37722}, \"VV\": {\"delta_db\": -0.877472}}}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=47c4fef16f82]\n\nLAST OBSERVATION\n{\"args\": {\"labels\": {\"concordant\": true, \"discordant\": true, \"no_change\": true}, \"question\": \"question_1\", \"rule\": \"concordance\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"class_1_answer_ref\": \"ANSWER_JSON:class_1\", \"class_1_evidence\": {\"evidence\": {\"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427}, \"predicted_class\": \"discordant\"}, \"class_1_payload\": {\"concordance\": \"discordant\", \"evidence\": {\"nbr_drop\": 0.025977, \"ndvi_drop\": 0.030427, \"vh_delta_db\": -1.57787}, \"predicted_class\": \"discordant\", \"rule\": \"concordance\", \"thresholds\": {\"optical_change_ndvi_drop_gt\": 0.1, \"sar_change_vh_delta_db_lt\": -1.0}}, \"class_1_payload_keys\": [\"rule\", \"concordance\", \"predicted_class\", \"evidence\", \"thresholds\"], \"class_1_summary\": \"classified change `class_1`\", \"latest_answer_ref\": \"ANSWER_JSON:class_1\", \"latest_handle\": \"class_1\", \"latest_summary\": \"classified change `class_1`\", \"latest_tool\": \"ClassifyChange\", \"latest_tool_ok\": true}, \"layers\": []}, \"text\": \"classifie...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 351, "id": "deforestation/001109/cross_modal_concordance", "dataset_id": "deforestation/001109/cross_modal_concordance", "sample_id": "001109", "question_type": "cross_modal_concordance", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources", "ClassifyChange"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "47c4fef16f827402d169733a432bbe2846ff38c9fdd61138fe7816032fb32139"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - Sentinel-1 GRD pre/post stack for VV/VH backscatter.; refs: s1_00.tif, s1_03.tif\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"sar_stack_1\"]},{\"id\":\"s2\",\"objective\":\"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_stack_1\",\"role\":\"pre_post\",\"time\":\"event_window\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_stack_1\", \"question\": \"question_1\", \"role\": \"pre_post\", \"time\": \"event_window\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_change` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_change\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_change\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"vh_delta_db\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"vh_delta_db\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.57787}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"vh_delta_db\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.57787}, \"stats_1_payload\": {\"canopy_class\": \"unknown\", \"loss_area_ha\": null, \"loss_year_histogram_ha\": {}, \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.57787, \"treecover2000_mean_pct\": null}, \"stats_1_payload_keys\": [\"metrics\", \"treecover2000_mean_pct\", \"canopy_class\", \"loss_area_ha\", \"loss_year_histogram_ha\", \"sar_vh_delta_db\"], \"stats_1_summary\": \"computed statistics `stats_1`\"}, \"layers\": []}, \"text\": \"computed statistics `stats_1`\", \"tool\": \"ComputeSta...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `forest_disturbance` on `vh_delta_severity` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"stats_1\",\"hazard\":\"forest_disturbance\",\"scale\":\"vh_delta_severity\"}}}"}, {"role": "user", "content": "QUESTION\nUsing all available Sentinel-1 acquisitions and their declared pre/post temporal roles, compute the whole-AOI mean VH backscatter change as post_mean_db minus pre_mean_db. Return that finite delta in decibels and bucket severity as severe for delta < -3, moderate for -3 <= delta < -1, mild for -1 <= delta < 0, or none for delta >= 0. Thus -3 is moderate, -1 is mild, and 0 is none.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the deterministic deforestation / severity proxy numeric task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_stack_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: prepare reusable evidence from sar_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"deterministic deforestation / severity proxy numeric: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_stack_1 [sar_stack] - sar_stack_1: Sentinel-1 GRD pre/post stack for VV/VH backscatter.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.57787, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"units\": \"dB\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- stats_1_evidence = {\"canopy_class\": \"unknown\", \"metrics\": {\"sar_change\": true, \"vh_delta_db\": true}, \"sar_vh_delta_db\": -1.57787}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f4d3b132c861]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"forest_disturbance\", \"question\": \"question_1\", \"scale\": \"vh_delta_severity\", \"work\": \"stats_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.57787, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"units\": \"dB\"}, \"score_1_payload\": {\"hazard\": \"forest_disturbance\", \"sar_vh_delta_db\": -1.57787, \"scale\": \"vh_delta_severity\", \"severity\": \"moderate\", \"severity_contract\": \"vh_delta_question_thresholds_v1\", \"severity_metric\": \"post_mean_db_minus_pre_mean_db\", \"thresholds_db\": {\"mild\": \"-1 <= delta < 0\", \"moderate\": \"-3 <= delta < -1\", \"none\": \"delta >= 0\", \"severe\": \"delta < -3\"}, \"units\": \"dB\", \"vh_delta_db\": -1.57787}, \"score_1_payload_keys\": [\"hazard\", \"scale\", \"vh_d...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "deforestation_data", "export_source": "geodisaster_test", "idx": 352, "id": "deforestation/001109/severity_proxy_numeric", "dataset_id": "deforestation/001109/severity_proxy_numeric", "sample_id": "001109", "question_type": "severity_proxy_numeric", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f4d3b132c861381b8d08706fb2eddfcd5e06dbca52cef2968f92dce788745a0f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `wildfire_burn` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"wildfire_burn\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"burned_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"burned_area\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"wildfire__000018\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 12390.582, \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bf781675f912]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"burned_area\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"wildfire__000018\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 12390.582, \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"wildfire__000018\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 12390.582, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\": \"computed sta...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 417, "id": "multi_hazard/wildfire/000018/A", "dataset_id": "multi_hazard/wildfire/000018/A", "sample_id": "wildfire/000018", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "bf781675f912b8698cda88fff10c49ac8fd95855d225cfa44f3d4fe17a902123"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `wildfire_burn` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"wildfire_burn\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `wildfire` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"wildfire\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"high\": {\"area_ha\": 3780.778, \"mean_value\": 0.612158, \"pixel_count\": 2565}, \"low\": {\"area_ha\": 3416.895, \"mean_value\": 0.176951, \"pixel_count\": 2318}, \"moderate\": {\"area_ha\": 2311.208, \"mean_value\": 0.351522, \"pixel_count\": 1568}, \"unburned\": {\"area_ha\": 2881.701, \"mean_value\": 0.020029, \"pixel_count\":...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7154259943eb]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"wildfire\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"high\": {\"area_ha\": 3780.778, \"mean_value\": 0.612158, \"pixel_count\": 2565}, \"low\": {\"area_ha\": 3416.895, \"mean_value\": 0.176951, \"pixel_count\": 2318}, \"moderate\": {\"area_ha\": 2311.208, \"mean_value\": 0.351522, \"pixel_count\": 1568}, \"unburned\": {\"area_ha\": 2881.701, \"mean_value\": 0.020029, \"pixel_count\": 1955}}, \"dominant_class\": \"high\", \"max\": 1.041305, \"mean\": 0.305817, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"mcd64_burn_footprint\", \"aoi_id\": \"wildfire__000018\", \"by_class\": {\"high\": {\"area_ha\": 3780.778, \"mean_value\": 0.612158, \"pixel_count\": 2565}, \"low\": {\"...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 418, "id": "multi_hazard/wildfire/000018/B", "dataset_id": "multi_hazard/wildfire/000018/B", "sample_id": "wildfire/000018", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7154259943eb3264b5a60edb71bae96b2d9bde3f79212dbdb789ba60152d980c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"wildfire\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"wildfire\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 20.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":20.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 12390.582, \"median_pct_delta\": 20.063, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"mcd64a1__nifc_perimeter\": 0.904936, \"mcd64a1__s2_burn_support\": 0.711054, \"s2_burn_support__nifc_perimeter\": 0.739977}, \"pairwise_correlation\": {\"mcd64a1__nifc_perimeter\": 0.904996...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=52dde9c1905e]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 20.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 12390.582, \"median_pct_delta\": 20.063, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"mcd64a1__nifc_perimeter\": 0.904936, \"mcd64a1__s2_burn_support\": 0.711054, \"s2_burn_support__nifc_perimeter\": 0.739977}, \"pairwise_correlation\": {\"mcd64a1__nifc_perimeter\": 0.904996, \"mcd64a1__s2_burn_support\": 0.723731, \"s2_burn_support__nifc_perimeter\": 0.754832}, \"pairwise_iou\": {\"mcd64a1__nifc_perimeter\": 0.912011, \"mcd64a1__s2_burn_support\": 0.730743, \"s2_burn_support__nifc_perimeter\": 0.755309}, \"pairwise_pct_delta\": {\"mcd64a1__nifc_perimeter\": 1.113, \"mcd64a1__s2_burn_support\": 20.063, \"s2_burn_support__nifc_perimeter\": 21.164}, \"pairwise_state_agreement\": {\"mcd64a1__nifc_...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 419, "id": "multi_hazard/wildfire/000018/C", "dataset_id": "multi_hazard/wildfire/000018/C", "sample_id": "wildfire/000018", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "52dde9c1905e3946c3b7ab4bab5ca33b2e973b50ea554485dbb72ff7396de6e1"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"highways\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"highways\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `wildfire_burn_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"wildfire_burn_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `wildfire` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"wildfire\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 12390.582, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"wildfire\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 12390.582, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}}, \"score_1_payload\": {\"aoi_id\": \"wildfire__000018\", \"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 12390.582, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"ScoreHa...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `highway_representatives_within_5km` exposure with metrics {\\\"within_km\\\": 5}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"highway_representatives_within_5km\",\"metrics\":{\"within_km\":5}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 232, \"in_footprint\": 92, \"nearest_distance_m\": 1.951, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 12390.582, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 17.98, \"population_within_footprin...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 12390.582, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8406}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=dd02056a387a]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"highway_representatives_within_5km\", \"metrics\": {\"within_km\": 5}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 232, \"in_footprint\": 92, \"nearest_distance_m\": 1.951, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 12390.582, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 17.98, \"population_within_footprint\": 15.226}, \"exposure_1_payload\": {\"aoi_id\": \"wildfire__000018\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"highways\": {\"in_buffer\": 232, \"in_footprint\": 92, \"nearest\": {\"distance_m\": 1.951, \"name\": \"Highways OSM\"}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 12390.582, \"footprint_handle\": \"mcd64a1__event\", \"populati...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 420, "id": "multi_hazard/wildfire/000018/D", "dataset_id": "multi_hazard/wildfire/000018/D", "sample_id": "wildfire/000018", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "dd02056a387ad28cb69ee53ed4f999bda16bca3aabf2ec68599bbb43f73033fa"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `wildfire_burn` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"wildfire_burn\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"burned_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"burned_area\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MODIS MCD64A1 event burned-area mask, compute the burned area for this AOI in hectares. Exclude non-finite and declared nodata pixels, treat mask value 1 as burned and 0 as valid unburned, sum grid-aware WGS84 ellipsoidal cell areas, and report the result rounded to three decimal places.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MODIS MCD64A1 event mask and metadata required for burned-area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"wildfire__000030\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 13218.958, \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b08fd8b19065]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"burned_area\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"wildfire__000030\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 13218.958, \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"wildfire__000030\", \"metrics\": {\"burned_area\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 13218.958, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\": \"computed sta...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 441, "id": "multi_hazard/wildfire/000030/A", "dataset_id": "multi_hazard/wildfire/000030/A", "sample_id": "wildfire/000030", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b08fd8b190650ec5b89f226778798ac5b0ffc8490dddc1f0eee4c0617964d925"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `wildfire_burn` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"wildfire_burn\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `wildfire` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"wildfire\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary MODIS MCD64A1 burn footprint, classify every valid Sentinel-2 dNBR pixel as unburned (<0.10), low (0.10 <= dNBR < 0.27), moderate (0.27 <= dNBR < 0.44), or high (dNBR >= 0.44). Return the dominant class, its fraction of all valid dNBR pixels inside the footprint, the per-class pixel counts/grid-aware areas/means, the analysis scope, and footprint pixel count. Break an equal-count tie by the declared order: unburned, low, moderate, high.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-2 dNBR plus binary MCD64A1 footprint and their metadata for masked severity classification.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"high\": {\"area_ha\": 6561.489, \"mean_value\": 0.725008, \"pixel_count\": 4438}, \"low\": {\"area_ha\": 2464.571, \"mean_value\": 0.18195, \"pixel_count\": 1667}, \"moderate\": {\"area_ha\": 1852.509, \"mean_value\": 0.351841, \"pixel_count\": 1253}, \"unburned\": {\"area_ha\": 2340.388, \"mean_value\": 0.021913, \"pixel_count\":...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9eeaf2f67f86]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"wildfire\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"high\": {\"area_ha\": 6561.489, \"mean_value\": 0.725008, \"pixel_count\": 4438}, \"low\": {\"area_ha\": 2464.571, \"mean_value\": 0.18195, \"pixel_count\": 1667}, \"moderate\": {\"area_ha\": 1852.509, \"mean_value\": 0.351841, \"pixel_count\": 1253}, \"unburned\": {\"area_ha\": 2340.388, \"mean_value\": 0.021913, \"pixel_count\": 1583}}, \"dominant_class\": \"high\", \"max\": 1.184176, \"mean\": 0.446979, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"mcd64_burn_footprint\", \"aoi_id\": \"wildfire__000030\", \"by_class\": {\"high\": {\"area_ha\": 6561.489, \"mean_value\": 0.725008, \"pixel_count\": 4438}, \"low\": {\"a...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 442, "id": "multi_hazard/wildfire/000030/B", "dataset_id": "multi_hazard/wildfire/000030/B", "sample_id": "wildfire/000030", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9eeaf2f67f8628827dcab501637c77fc86f27f65958859ffe92e0d5210b920b8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"wildfire\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"wildfire\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 20.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":20.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared binary MODIS MCD64A1 burn mask, fixed-threshold Sentinel-2 dNBR burn-support mask, and NIFC incident-perimeter mask. Compute each grid-aware area and each pair's symmetric absolute percent difference, defined as |A-B| / ((A+B)/2) * 100 (0 when both are zero). Use the median of the three pairwise differences: the sources agree when it is <=20.0%. Return the decision, median area, median percent difference, bounded score max(0, 100-median difference), all source areas, and the pairwise percent-difference/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: The three binary, co-registered source masks and their provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Wildfire comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"high\", \"median_area_ha\": 13218.958, \"median_pct_delta\": 16.307, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"mcd64a1__nifc_perimeter\": 0.933473, \"mcd64a1__s2_burn_support\": 0.77955, \"s2_burn_support__nifc_perimeter\": 0.78618}, \"pairwise_correlation\": {\"mcd64a1__nifc_perimeter\": 0.933866,...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=273064740804]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 20.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"high\", \"median_area_ha\": 13218.958, \"median_pct_delta\": 16.307, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"mcd64a1__nifc_perimeter\": 0.933473, \"mcd64a1__s2_burn_support\": 0.77955, \"s2_burn_support__nifc_perimeter\": 0.78618}, \"pairwise_correlation\": {\"mcd64a1__nifc_perimeter\": 0.933866, \"mcd64a1__s2_burn_support\": 0.790178, \"s2_burn_support__nifc_perimeter\": 0.801013}, \"pairwise_iou\": {\"mcd64a1__nifc_perimeter\": 0.942217, \"mcd64a1__s2_burn_support\": 0.801874, \"s2_burn_support__nifc_perimeter\": 0.809344}, \"pairwise_pct_delta\": {\"mcd64a1__nifc_perimeter\": 2.593, \"mcd64a1__s2_burn_support\": 16.307, \"s2_burn_support__nifc_perimeter\": 18.88}, \"pairwise_state_agreement\": {\"mcd64a1__nifc_pe...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 443, "id": "multi_hazard/wildfire/000030/C", "dataset_id": "multi_hazard/wildfire/000030/C", "sample_id": "wildfire/000030", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2730647408040db671b9b0dc1d4fa45d7f2492530e3dc231ea6a57b32bbeeffe"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"wildfire\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"wildfire\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"highways\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"highways\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `wildfire_burn_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"wildfire_burn_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"wildfire_burn_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `wildfire` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"wildfire\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 13218.958, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"wildfire\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 13218.958, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}}, \"score_1_payload\": {\"aoi_id\": \"wildfire__000030\", \"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 13218.958, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"ScoreHa...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `highway_representatives_within_5km` exposure with metrics {\\\"within_km\\\": 5}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"highway_representatives_within_5km\",\"metrics\":{\"within_km\":5}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary MCD64A1 burn footprint and cached OSM highway features, count the distinct feature representative points whose exact GeoJSON geometry is within an inclusive 5,000 m of the nearest burn-mask cell center. Use a local azimuthal-equidistant metric projection; do not use cached row/column hints. Return the count plus the footprint area, WorldPop footprint sum, in-footprint count, nearest representative, distance method, and geometry contract.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard wildfire / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard wildfire / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and cached OSM feature geometries required by the exposure calculation.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary MCD64A1 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 64, \"in_footprint\": 37, \"nearest_distance_m\": 10.037, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 13218.958, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\"...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"wildfire\", \"impact_footprint\": {\"area_ha\": 13218.958, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"mcd64a1__event\", \"pixel_count\": 8941}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=74ea091bc8d2]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"highway_representatives_within_5km\", \"metrics\": {\"within_km\": 5}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 64, \"in_footprint\": 37, \"nearest_distance_m\": 10.037, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 13218.958, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"wildfire__000030\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"highways\": {\"in_buffer\": 64, \"in_footprint\": 37, \"nearest\": {\"distance_m\": 10.037, \"name\": \"Highways OSM\"}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 13218.958, \"footprint_handle\": \"mcd64a1__event\", \"population_so...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 444, "id": "multi_hazard/wildfire/000030/D", "dataset_id": "multi_hazard/wildfire/000030/D", "sample_id": "wildfire/000030", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "74ea091bc8d2351ca87c1ab36183dfae269ff5c4c805e5bce29890de1591bd8d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `forest_loss` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"forest_loss\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"forest_loss\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"forest_loss\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"deforestation__000010\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1449.402, \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=00c61b612efa]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"forest_loss\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"deforestation__000010\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1449.402, \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"deforestation__000010\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1449.402, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\":...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 469, "id": "multi_hazard/deforestation/000010/A", "dataset_id": "multi_hazard/deforestation/000010/A", "sample_id": "deforestation/000010", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "00c61b612efa69b3f3ebc2be4ae35524212b587afe9967dd9f04daddd305d8d4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `forest_loss` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"forest_loss\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `deforestation` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"deforestation\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"major\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"minor\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"none\": {\"area_ha\": 1449.402, \"mean_value\": -0.286088, \"pixel_count\": 767}, \"severe\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"none\", \"max...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5aeb0206f868]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"deforestation\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"major\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"minor\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"none\": {\"area_ha\": 1449.402, \"mean_value\": -0.286088, \"pixel_count\": 767}, \"severe\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"none\", \"max\": 0.040465, \"mean\": -0.286088, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"hansen_loss_footprint\", \"aoi_id\": \"deforestation__000010\", \"by_class\": {\"major\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"minor\": {\"area_ha\": 0.0, \"mean_value\": null, \"...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 470, "id": "multi_hazard/deforestation/000010/B", "dataset_id": "multi_hazard/deforestation/000010/B", "sample_id": "deforestation/000010", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5aeb0206f868c345bae7a1175db31c9b9003a8617d165170a257247634406d31"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"deforestation\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"deforestation\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 25.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":25.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 274.006, \"median_pct_delta\": 136.404, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"hansen_loss__radd_alerts\": 0.003727, \"hansen_loss__tmf_loss\": 0.25425, \"tmf_loss__radd_alerts\": -0.00476}, \"pairwise_correlation\": {\"hansen_loss__radd_alerts\": 0.007729, \"hansen_los...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d2f3b6a836c8]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 25.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 274.006, \"median_pct_delta\": 136.404, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"hansen_loss__radd_alerts\": 0.003727, \"hansen_loss__tmf_loss\": 0.25425, \"tmf_loss__radd_alerts\": -0.00476}, \"pairwise_correlation\": {\"hansen_loss__radd_alerts\": 0.007729, \"hansen_loss__tmf_loss\": 0.352348, \"tmf_loss__radd_alerts\": -0.005383}, \"pairwise_iou\": {\"hansen_loss__radd_alerts\": 0.004902, \"hansen_loss__tmf_loss\": 0.152971, \"tmf_loss__radd_alerts\": 0.0}, \"pairwise_pct_delta\": {\"hansen_loss__radd_alerts\": 174.147, \"hansen_loss__tmf_loss\": 136.404, \"tmf_loss__radd_alerts\": 92.932}, \"pairwise_state_agreement\": {\"hansen_loss__radd_alerts\": 0.950439, \"hansen_loss__tmf_loss\": 0.9...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 471, "id": "multi_hazard/deforestation/000010/C", "dataset_id": "multi_hazard/deforestation/000010/C", "sample_id": "deforestation/000010", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d2f3b6a836c8f1b5a31494c19b00460844df4b74565905ed7e89bdfcfaac774f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"nearest_highway\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"nearest_highway\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `forest_loss_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"forest_loss_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"forest_loss_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `deforestation` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"deforestation\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 1449.402, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"deforestation\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 1449.402, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}}, \"score_1_payload\": {\"aoi_id\": \"deforestation__000010\", \"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 1449.402, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `sco...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `nearest_highway` exposure with metrics {\\\"distance_to\\\": \\\"nearest_highway\\\"}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"nearest_highway\",\"metrics\":{\"distance_to\":\"nearest_highway\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"highways\"}], \"footprint_area_ha\": 1449.402, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 3.712, \"population_within_footprint\": 0.0}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 1449.402, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 767}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2f4f9f9a423f]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"nearest_highway\", \"metrics\": {\"distance_to\": \"nearest_highway\"}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"highways\"}], \"footprint_area_ha\": 1449.402, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 3.712, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"deforestation__000010\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"highways\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 1449.402, \"footprint_handle\": \"hansen_loss__event\", \"population_source\": \"WorldPop/GP/...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 472, "id": "multi_hazard/deforestation/000010/D", "dataset_id": "multi_hazard/deforestation/000010/D", "sample_id": "deforestation/000010", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2f4f9f9a423f388c314e02a85a5953686a88555cc5c259954f181acc6d73c52c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `forest_loss` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"forest_loss\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"forest_loss\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"forest_loss\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen Global Forest Change event mask, compute the AOI area whose loss year equals 2022. Treat finite value 1 as 2022 loss and 0 as valid no-2022-loss, exclude declared nodata, sum grid-aware WGS84 ellipsoidal cell areas, and report hectares rounded to three decimals.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 loss-year mask and metadata required for exact area measurement.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"deforestation__000012\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 107.692, \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e852b9d37e36]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"forest_loss\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"deforestation__000012\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 107.692, \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"deforestation__000012\", \"metrics\": {\"forest_loss\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 107.692, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\": \"co...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 477, "id": "multi_hazard/deforestation/000012/A", "dataset_id": "multi_hazard/deforestation/000012/A", "sample_id": "deforestation/000012", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e852b9d37e369e4b3d3b38b8dc12c65b4d10d34d9b06d97d12c611a07188c6c9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `forest_loss` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"forest_loss\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"forest_loss\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `deforestation` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"deforestation\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nInside the declared binary Hansen 2022 loss footprint, classify every finite Sentinel-2 NDVI drop (pre minus post) using half-open bins: none [-10,0.15), minor [0.15,0.30), major [0.30,0.50), and severe [0.50,10). Return the pixel-count-dominant class and its fraction; break an exact tie by the stated class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Co-registered Hansen 2022 loss mask and Sentinel-2 NDVI pre-minus-post raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"major\": {\"area_ha\": 1.923, \"mean_value\": 0.309156, \"pixel_count\": 1}, \"minor\": {\"area_ha\": 3.846, \"mean_value\": 0.227241, \"pixel_count\": 2}, \"none\": {\"area_ha\": 101.923, \"mean_value\": -0.178792, \"pixel_count\": 53}, \"severe\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"n...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8dd9abc1c5c7]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"deforestation\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"major\": {\"area_ha\": 1.923, \"mean_value\": 0.309156, \"pixel_count\": 1}, \"minor\": {\"area_ha\": 3.846, \"mean_value\": 0.227241, \"pixel_count\": 2}, \"none\": {\"area_ha\": 101.923, \"mean_value\": -0.178792, \"pixel_count\": 53}, \"severe\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"none\", \"max\": 0.309156, \"mean\": -0.155577, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"hansen_loss_footprint\", \"aoi_id\": \"deforestation__000012\", \"by_class\": {\"major\": {\"area_ha\": 1.923, \"mean_value\": 0.309156, \"pixel_count\": 1}, \"minor\": {\"area_ha\": 3.846, \"me...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 478, "id": "multi_hazard/deforestation/000012/B", "dataset_id": "multi_hazard/deforestation/000012/B", "sample_id": "deforestation/000012", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8dd9abc1c5c74b937221dca80cf5d298fe7e47fc2a5f3036c8dd2ce05f5b2347"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"deforestation\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"deforestation\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 25.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":25.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare the declared Hansen 2022 loss mask, JRC TMF 2022 annual-loss mask, and RADD 2022-06-01 through 2022-09-30 alert mask (low or high confidence). Compute grid-aware source areas and symmetric pairwise differences |A-B|/((A+B)/2)*100, using 0 when both areas are zero. The sources agree when the median of the three differences is <=25.0%. Return the decision, bounded score max(0,100-median), all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source masks and their temporal/provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Deforestation comparison context and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 3.846, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"hansen_loss__radd_alerts\": 0.0, \"hansen_loss__tmf_loss\": 0.034255, \"tmf_loss__radd_alerts\": 0.0}, \"pairwise_correlation\": {\"hansen_loss__radd_alerts\": null, \"hansen_loss__tmf_loss\": 0.0...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1c5ba84241cc]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 25.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 3.846, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"hansen_loss__radd_alerts\": 0.0, \"hansen_loss__tmf_loss\": 0.034255, \"tmf_loss__radd_alerts\": 0.0}, \"pairwise_correlation\": {\"hansen_loss__radd_alerts\": null, \"hansen_loss__tmf_loss\": 0.094012, \"tmf_loss__radd_alerts\": null}, \"pairwise_iou\": {\"hansen_loss__radd_alerts\": 0.0, \"hansen_loss__tmf_loss\": 0.017544, \"tmf_loss__radd_alerts\": 0.0}, \"pairwise_pct_delta\": {\"hansen_loss__radd_alerts\": 200.0, \"hansen_loss__tmf_loss\": 186.207, \"tmf_loss__radd_alerts\": 200.0}, \"pairwise_state_agreement\": {\"hansen_loss__radd_alerts\": 0.996582, \"hansen_loss__tmf_loss\": 0.996582, \"tmf_loss__radd_alerts\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 479, "id": "multi_hazard/deforestation/000012/C", "dataset_id": "multi_hazard/deforestation/000012/C", "sample_id": "deforestation/000012", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1c5ba84241ccaebbe2a70cbc31040fc89e5b187a1d9d20a1fd682942204d0039"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"deforestation\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"deforestation\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"nearest_highway\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"nearest_highway\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `forest_loss_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"forest_loss_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"forest_loss_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `deforestation` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"deforestation\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 107.692, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"deforestation\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 107.692, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}}, \"score_1_payload\": {\"aoi_id\": \"deforestation__000012\", \"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 107.692, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `nearest_highway` exposure with metrics {\\\"distance_to\\\": \\\"nearest_highway\\\"}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"nearest_highway\",\"metrics\":{\"distance_to\":\"nearest_highway\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Hansen 2022 forest-loss footprint and cached OSM highway features, find the nearest distinct feature representative point to any positive loss-raster cell center in a local azimuthal-equidistant metric projection. Use exact GeoJSON geometry, not cached row/column hints. Return the distance or an explicit null when no highway representative is cached, plus the 5,000 m context count, in-footprint count, nearest name, footprint area, and WorldPop footprint sum.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard deforestation / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard deforestation / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Hansen 2022 impact footprint, metadata, and exact cached-handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 4, \"in_footprint\": 0, \"nearest_distance_m\": 2566.039, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 107.692, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 762.178, \"population_within_footprin...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"deforestation\", \"impact_footprint\": {\"area_ha\": 107.692, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"hansen_loss__event\", \"pixel_count\": 56}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=87165623a069]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"nearest_highway\", \"metrics\": {\"distance_to\": \"nearest_highway\"}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 4, \"in_footprint\": 0, \"nearest_distance_m\": 2566.039, \"nearest_name\": \"Highways OSM\", \"type\": \"highways\"}], \"footprint_area_ha\": 107.692, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 762.178, \"population_within_footprint\": 6.565}, \"exposure_1_payload\": {\"aoi_id\": \"deforestation__000012\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"highways\": {\"in_buffer\": 4, \"in_footprint\": 0, \"nearest\": {\"distance_m\": 2566.039, \"name\": \"Highways OSM\"}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 107.692, \"footprint_handle\": \"hansen_loss__event\", \"pop...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 480, "id": "multi_hazard/deforestation/000012/D", "dataset_id": "multi_hazard/deforestation/000012/D", "sample_id": "deforestation/000012", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "87165623a0699638fa0471b80e9c3e9bbe9b824c21fa08311cb25f58d5ba969a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `built_up_gain` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"built_up_gain\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"built_up_gain\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"built_up_gain\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"urban_expansion__000025\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1985.341, \"handle\": \"built_gain\", \"pixel_count\": 1058}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a0bc20c6e5ae]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"built_up_gain\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"urban_expansion__000025\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1985.341, \"handle\": \"built_gain\", \"pixel_count\": 1058}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"urban_expansion__000025\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 1985.341, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1058}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\": \"co...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 633, "id": "multi_hazard/urban_expansion/000025/A", "dataset_id": "multi_hazard/urban_expansion/000025/A", "sample_id": "urban_expansion/000025", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a0bc20c6e5ae4de1b5fb5e09f5a899351352b7d4f5e66620a192e4f56aa67fc5"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `built_up_gain` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"built_up_gain\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `urban_expansion` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"urban_expansion\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"contraction\": {\"area_ha\": 12548.974, \"mean_value\": -0.056767, \"pixel_count\": 6687}, \"major\": {\"area_ha\": 347.211, \"mean_value\": 0.259477, \"pixel_count\": 185}, \"minor\": {\"area_ha\": 7638.507, \"mean_value\": 0.056233, \"pixel_count\": 4070}, \"stable\": {\"area_ha\": 10212.922, \"mean_value\": -0.001174, \"pixel_c...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bd24129e1133]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"urban_expansion\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"contraction\": {\"area_ha\": 12548.974, \"mean_value\": -0.056767, \"pixel_count\": 6687}, \"major\": {\"area_ha\": 347.211, \"mean_value\": 0.259477, \"pixel_count\": 185}, \"minor\": {\"area_ha\": 7638.507, \"mean_value\": 0.056233, \"pixel_count\": 4070}, \"stable\": {\"area_ha\": 10212.922, \"mean_value\": -0.001174, \"pixel_count\": 5442}}, \"dominant_class\": \"contraction\", \"max\": 0.581142, \"mean\": -0.00666, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"urban_expansion__000025\", \"by_class\": {\"contraction\": {\"area_ha\": 12548.974, \"mean_value\": -0.056767, \"pixel...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 634, "id": "multi_hazard/urban_expansion/000025/B", "dataset_id": "multi_hazard/urban_expansion/000025/B", "sample_id": "urban_expansion/000025", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "bd24129e11334122feb9b489e1951e62660d0064b7b6623678e12f5885fd24b3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Urban comparison context and exact handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"urban_expansion\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"urban_expansion\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 30.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":30.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 1429.996, \"median_pct_delta\": 69.265, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"built_gain__ghsl_built_gain\": 0.017186, \"built_gain__worldcover_built_gain\": 0.042178, \"ghsl_built_gain__worldcover_built_gain\": 0.023309}, \"pairwise_correlation\": {\"built_gain__ghs...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2c1dccdb3020]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 30.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 1429.996, \"median_pct_delta\": 69.265, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"built_gain__ghsl_built_gain\": 0.017186, \"built_gain__worldcover_built_gain\": 0.042178, \"ghsl_built_gain__worldcover_built_gain\": 0.023309}, \"pairwise_correlation\": {\"built_gain__ghsl_built_gain\": 0.017446, \"built_gain__worldcover_built_gain\": 0.048653, \"ghsl_built_gain__worldcover_built_gain\": 0.024955}, \"pairwise_iou\": {\"built_gain__ghsl_built_gain\": 0.036446, \"built_gain__worldcover_built_gain\": 0.038545, \"ghsl_built_gain__worldcover_built_gain\": 0.027223}, \"pairwise_pct_delta\": {\"built_gain__ghsl_built_gain\": 32.521, \"built_gain__worldcover_built_gain\": 96.359, \"ghsl_built_gai...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 635, "id": "multi_hazard/urban_expansion/000025/C", "dataset_id": "multi_hazard/urban_expansion/000025/C", "sample_id": "urban_expansion/000025", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2c1dccdb30200547b7360a63ee3097e2328c5a75eda229df4f1b7bde8e780269"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"population_in_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"population_in_footprint\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `built_up_gain_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"built_up_gain_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `urban_expansion` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"urban_expansion\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 1985.341, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1058}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"urban_expansion\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 1985.341, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1058}}, \"score_1_payload\": {\"aoi_id\": \"urban_expansion__000025\", \"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 1985.341, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1058}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `population_in_footprint` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"population_in_footprint\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 12, \"in_footprint\": 1, \"nearest_distance_m\": 55.515, \"nearest_name\": \"Kaggalipura Government Hospital\", \"type\": \"hospitals\"}, {\"in_buffer\": 12, \"in_footprint\": 0, \"nearest_distance_m\": 154.249, \"nearest_name\": \"Cohesion Montessori\", \"type\": \"schools\"}], \"footprint_area_ha\":...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 1985.341, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1058}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=53da3abe989d]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"population_in_footprint\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 12, \"in_footprint\": 1, \"nearest_distance_m\": 55.515, \"nearest_name\": \"Kaggalipura Government Hospital\", \"type\": \"hospitals\"}, {\"in_buffer\": 12, \"in_footprint\": 0, \"nearest_distance_m\": 154.249, \"nearest_name\": \"Cohesion Montessori\", \"type\": \"schools\"}], \"footprint_area_ha\": 1985.341, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 54295.659, \"population_within_footprint\": 4112.71}, \"exposure_1_payload\": {\"aoi_id\": \"urban_expansion__000025\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"hospitals\": {\"in_buffer\": 12, \"in_footprint\": 1, \"nearest\": {\"distance_m\": 55.515, \"name\": \"Kaggalipura Gov...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 636, "id": "multi_hazard/urban_expansion/000025/D", "dataset_id": "multi_hazard/urban_expansion/000025/D", "sample_id": "urban_expansion/000025", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "53da3abe989d6772a48477746ebc45a6669862959cbe67241db12f9731730ecd"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `built_up_gain` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"built_up_gain\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"built_up_gain\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"built_up_gain\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World built-probability gain mask, compute new built-up area between the 2019 and 2024 annual composites. A gain pixel has 2024 built probability >0.55 and 2019 built probability <=0.55. Sum exact grid-cell areas and return hectares with the source handle.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World 2019-to-2024 built-gain mask, provenance/grid metadata, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"urban_expansion__000030\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 3557.304, \"handle\": \"built_gain\", \"pixel_count\": 1850}, \"region\": \"aoi\"}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a240a4007054]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"built_up_gain\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"urban_expansion__000030\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 3557.304, \"handle\": \"built_gain\", \"pixel_count\": 1850}, \"region\": \"aoi\"}, \"stats_1_payload\": {\"aoi_id\": \"urban_expansion__000030\", \"metrics\": {\"built_up_gain\": true, \"hazard_area\": true}, \"primary_footprint\": {\"area_ha\": 3557.304, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1850}, \"region\": \"aoi\"}, \"stats_1_payload_keys\": [\"aoi_id\", \"region\", \"metrics\", \"primary_footprint\"], \"stats_1_summary\": \"co...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 649, "id": "multi_hazard/urban_expansion/000030/A", "dataset_id": "multi_hazard/urban_expansion/000030/A", "sample_id": "urban_expansion/000030", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a240a4007054db700a11cd0f0cb6531b5a9936b755c12a5c0c1304cc3c36b856"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `built_up_gain` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"built_up_gain\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `urban_expansion` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"urban_expansion\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nClassify every finite whole-AOI Sentinel-2 NDBI change pixel (2024 minus 2019). Use contraction [-10,-0.02), stable [-0.02,0.02), minor [0.02,0.18), and major [0.18,10). Return the pixel-count-dominant class, its fraction, and complete per-class pixel/area/mean evidence; ties follow the declared class order.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Whole-AOI Sentinel-2 NDBI post-minus-pre raster, grid/provenance sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"contraction\": {\"area_ha\": 25097.27, \"mean_value\": -0.09311, \"pixel_count\": 13052}, \"major\": {\"area_ha\": 24.998, \"mean_value\": 0.216109, \"pixel_count\": 13}, \"minor\": {\"area_ha\": 2288.213, \"mean_value\": 0.054469, \"pixel_count\": 1190}, \"stable\": {\"area_ha\": 4093.785, \"mean_value\": -0.00244, \"pixel_count\"...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=23152aed192e]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"urban_expansion\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"contraction\": {\"area_ha\": 25097.27, \"mean_value\": -0.09311, \"pixel_count\": 13052}, \"major\": {\"area_ha\": 24.998, \"mean_value\": 0.216109, \"pixel_count\": 13}, \"minor\": {\"area_ha\": 2288.213, \"mean_value\": 0.054469, \"pixel_count\": 1190}, \"stable\": {\"area_ha\": 4093.785, \"mean_value\": -0.00244, \"pixel_count\": 2129}}, \"dominant_class\": \"contraction\", \"max\": 0.271272, \"mean\": -0.070364, \"units\": \"index\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"urban_expansion__000030\", \"by_class\": {\"contraction\": {\"area_ha\": 25097.27, \"mean_value\": -0.09311, \"pixel_count...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 650, "id": "multi_hazard/urban_expansion/000030/B", "dataset_id": "multi_hazard/urban_expansion/000030/B", "sample_id": "urban_expansion/000030", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "23152aed192e64bbec94fb9a25dcf1745e81558579e1ce8e35ab51e78fb4ca47"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - Urban comparison context and exact handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"urban_expansion\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"urban_expansion\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"area_agreement\\\", \\\"maximum_median_pct_delta\\\": 30.0} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"area_agreement\",\"maximum_median_pct_delta\":30.0}}}}"}, {"role": "user", "content": "QUESTION\nCompare source-native built-gain masks from Dynamic World 2019-2024, GHSL 2020-2025, and ESA WorldCover 2020-2021. Compute exact source areas and symmetric pairwise differences abs(A-B)/((A+B)/2)*100 (zero when both areas are zero). Sources agree when the median difference is <=30%. Return the decision, bounded max(0,100-median) score, all source areas, and pairwise percent/IoU/state-agreement/kappa evidence.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered source-native gain masks and provenance/grid metadata.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Urban comparison context and exact handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 2268.975, \"median_pct_delta\": 44.225, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"built_gain__ghsl_built_gain\": -0.053523, \"built_gain__worldcover_built_gain\": -0.007411, \"ghsl_built_gain__worldcover_built_gain\": -0.059645}, \"pairwise_correlation\": {\"built_gain__...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e65b1d90bcf3]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"area_agreement\", \"maximum_median_pct_delta\": 30.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 2268.975, \"median_pct_delta\": 44.225, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"built_gain__ghsl_built_gain\": -0.053523, \"built_gain__worldcover_built_gain\": -0.007411, \"ghsl_built_gain__worldcover_built_gain\": -0.059645}, \"pairwise_correlation\": {\"built_gain__ghsl_built_gain\": -0.055706, \"built_gain__worldcover_built_gain\": -0.007639, \"ghsl_built_gain__worldcover_built_gain\": -0.059686}, \"pairwise_iou\": {\"built_gain__ghsl_built_gain\": 0.017937, \"built_gain__worldcover_built_gain\": 0.042312, \"ghsl_built_gain__worldcover_built_gain\": 0.007064}, \"pairwise_pct_delta\": {\"built_gain__ghsl_built_gain\": 50.762, \"built_gain__worldcover_built_gain\": 44.225, \"ghsl_bui...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 651, "id": "multi_hazard/urban_expansion/000030/C", "dataset_id": "multi_hazard/urban_expansion/000030/C", "sample_id": "urban_expansion/000030", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e65b1d90bcf3cb8f0874e8b4cbc26c6be3eb6e291bcaddd565ef035728159a8d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"urban_expansion\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"urban_expansion\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"population_in_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"population_in_footprint\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `built_up_gain_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"built_up_gain_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"built_up_gain_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `urban_expansion` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"urban_expansion\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 3557.304, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1850}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"urban_expansion\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 3557.304, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1850}}, \"score_1_payload\": {\"aoi_id\": \"urban_expansion__000030\", \"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 3557.304, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1850}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `population_in_footprint` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"population_in_footprint\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the declared binary Dynamic World 2019-2024 built-gain footprint, sum the co-registered WorldPop 2020 people-per-pixel values inside positive gain cells. Return that population count plus footprint area, source label, and cached hospital/school representative-point context. This is population located in the footprint at the WorldPop reference epoch, not population growth caused by expansion.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard urban expansion / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard urban expansion / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 count grid and authoritative cached OSM feature geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary Dynamic World built-gain footprint, sidecar, and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 236, \"in_footprint\": 6, \"nearest_distance_m\": 35.421, \"nearest_name\": \"Kabete Vet Lab Secondary School\", \"type\": \"schools\"}, {\"in_buffer\": 1, \"in_footprint\": 0, \"nearest_distance_m\": 278.494, \"nearest_name\": \"P.C.E.A Kikuyu Hospital\", \"type\": \"hospitals\"}], \"footprint_area_...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"urban_expansion\", \"impact_footprint\": {\"area_ha\": 3557.304, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"built_gain\", \"pixel_count\": 1850}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c7c69ad8e44a]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"population_in_footprint\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 236, \"in_footprint\": 6, \"nearest_distance_m\": 35.421, \"nearest_name\": \"Kabete Vet Lab Secondary School\", \"type\": \"schools\"}, {\"in_buffer\": 1, \"in_footprint\": 0, \"nearest_distance_m\": 278.494, \"nearest_name\": \"P.C.E.A Kikuyu Hospital\", \"type\": \"hospitals\"}], \"footprint_area_ha\": 3557.304, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 381194.17, \"population_within_footprint\": 28480.515}, \"exposure_1_payload\": {\"aoi_id\": \"urban_expansion__000030\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"hospitals\": {\"in_buffer\": 1, \"in_footprint\": 0, \"nearest\": {\"distance_m\": 278.494, \"name\": \"P.C.E.A K...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 652, "id": "multi_hazard/urban_expansion/000030/D", "dataset_id": "multi_hazard/urban_expansion/000030/D", "sample_id": "urban_expansion/000030", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c7c69ad8e44a55410273b9a1e73da6820ec32613ea209a9bc7ef771ebfa2adf6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for California Central Valley drought AOI 18 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for California Central Valley drought AOI 18 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for California Central Valley drought AOI 18 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for California Central Valley drought AOI 18 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"ndvi_z_score\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"ndvi_z_score\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for California Central Valley drought AOI 18 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000018\", \"baseline_mean\": 0.450554, \"baseline_std\": 0.026649, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.468616, \"z_score\": 0.677786}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2522ca5a3793]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"ndvi_z_score\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000018\", \"baseline_mean\": 0.450554, \"baseline_std\": 0.026649, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.468616, \"z_score\": 0.677786}, \"stats_1_payload\": {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000018\", \"baseline_mean\": 0.450554, \"baseline_std\": 0.026649, \"baseline_years_used\": 6, \"method\": \"z_score\", \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"n_baseline_observations\": 6, \"target_date\": \"2024-08-15\", \"target...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 697, "id": "multi_hazard/drought/000018/A", "dataset_id": "multi_hazard/drought/000018/A", "sample_id": "drought/000018", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2522ca5a3793cb75edcb4db1dce6f2e63bf0cfe3f61562773190da360a2f9c2c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over California Central Valley drought AOI 18 on 2024-08-15?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over California Central Valley drought AOI 18 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over California Central Valley drought AOI 18 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over California Central Valley drought AOI 18 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over California Central Valley drought AOI 18 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 2481.492, \"mean_value\": -0.041437, \"pixel_count\": 1600}, \"q3_mid_high_spi\": {\"area_ha\": 15579.592, \"mean_value\": 1.325197, \"pixel_count\": 10048}, \"q4_high_spi\": {\"area_ha\": 7336.016, \"mean_value\": 1.5744...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e70f955d6a06]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 2481.492, \"mean_value\": -0.041437, \"pixel_count\": 1600}, \"q3_mid_high_spi\": {\"area_ha\": 15579.592, \"mean_value\": 1.325197, \"pixel_count\": 10048}, \"q4_high_spi\": {\"area_ha\": 7336.016, \"mean_value\": 1.574433, \"pixel_count\": 4736}}, \"dominant_class\": \"q3_mid_high_spi\", \"max\": 1.862008, \"mean\": 1.263782, \"units\": \"z_score\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"drought__000018\", \"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 698, "id": "multi_hazard/drought/000018/B", "dataset_id": "multi_hazard/drought/000018/B", "sample_id": "drought/000018", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e70f955d6a066d71459bf70dffb5461cea0d1be1f40e2f8199ccdccb336ca49d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - Train-only drought stress normalization and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"drought\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"drought\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"iou\\\", \\\"minimum_iou\\\": 0.3, \\\"threshold_contract\\\": \\\"per_source_train_pixel_q25_v1\\\", \\\"normalization_fit_split\\\": \\\"train\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"iou\",\"minimum_iou\":0.3,\"threshold_contract\":\"per_source_train_pixel_q25_v1\",\"normalization_fit_split\":\"train\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 7394.163, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": -0.100412}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"c...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ffca927705d8]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"iou\", \"minimum_iou\": 0.3, \"normalization_fit_split\": \"train\", \"sources\": \"all\", \"threshold_contract\": \"per_source_train_pixel_q25_v1\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 7394.163, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": -0.100412}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"chirps_spi__modis_ndvi\": null, \"modis_ndvi__era5_soil_moisture\": -0.109507}, \"pairwise_iou\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.177262}, \"pairwise_pct_delta\": {\"chirps_spi__era5_soil_moisture\": 200.0, \"chirps_spi__modis_ndvi\": 200.0, \"modis_ndvi__era5_soil_moisture\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 699, "id": "multi_hazard/drought/000018/C", "dataset_id": "multi_hazard/drought/000018/C", "sample_id": "drought/000018", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ffca927705d8dba0df046b894aae6028452306bb5df1414fb022e24df33aff24"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"schools\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"schools\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `drought_anomaly_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"drought_anomaly_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload\": {\"aoi_id\": \"drought__000018\", \"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"ScoreHazard\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `schools` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"schools\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for California Central Valley drought AOI 18?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=82e8026e2017]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"schools\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"drought__000018\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"schools\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 0.0, \"footprint_handle\": \"low_chirps_spi\", \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 700, "id": "multi_hazard/drought/000018/D", "dataset_id": "multi_hazard/drought/000018/D", "sample_id": "drought/000018", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "82e8026e20179c0db185619f4c360533580cc2940c1c021067b8358f5c97ca9c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 25 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 25 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 25 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 25 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"ndvi_z_score\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"ndvi_z_score\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 25 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000025\", \"baseline_mean\": 0.319112, \"baseline_std\": 0.053687, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.347356, \"z_score\": 0.526081}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=08093b800704]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"ndvi_z_score\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000025\", \"baseline_mean\": 0.319112, \"baseline_std\": 0.053687, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.347356, \"z_score\": 0.526081}, \"stats_1_payload\": {\"anomaly_class\": \"normal\", \"aoi_id\": \"drought__000025\", \"baseline_mean\": 0.319112, \"baseline_std\": 0.053687, \"baseline_years_used\": 6, \"method\": \"z_score\", \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"n_baseline_observations\": 6, \"target_date\": \"2024-08-15\", \"target...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 717, "id": "multi_hazard/drought/000025/A", "dataset_id": "multi_hazard/drought/000025/A", "sample_id": "drought/000025", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "08093b8007040dc599ccb89cebd3283a4431f89fccb1105a416d9132e80509e4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 25 on 2024-08-15?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 25 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 25 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 25 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 25 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q3_mid_high_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q4_high_spi\": {\"area_ha\": 31456.545, \"mean_value\": 4.219783, \"pixel_count\": 16384}}...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43161c1ecf79]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q3_mid_high_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q4_high_spi\": {\"area_ha\": 31456.545, \"mean_value\": 4.219783, \"pixel_count\": 16384}}, \"dominant_class\": \"q4_high_spi\", \"max\": 8.11379, \"mean\": 4.219783, \"units\": \"z_score\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"drought__000025\", \"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 718, "id": "multi_hazard/drought/000025/B", "dataset_id": "multi_hazard/drought/000025/B", "sample_id": "drought/000025", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43161c1ecf79f591c818cc1b1adc492fb32b1c88352c7bb4f6deb53821ffa355"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - Train-only drought stress normalization and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"drought\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"drought\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"iou\\\", \\\"minimum_iou\\\": 0.3, \\\"threshold_contract\\\": \\\"per_source_train_pixel_q25_v1\\\", \\\"normalization_fit_split\\\": \\\"train\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"iou\",\"minimum_iou\":0.3,\"threshold_contract\":\"per_source_train_pixel_q25_v1\",\"normalization_fit_split\":\"train\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 22264.098, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.0}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"chirps...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=22c3ff882924]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"iou\", \"minimum_iou\": 0.3, \"normalization_fit_split\": \"train\", \"sources\": \"all\", \"threshold_contract\": \"per_source_train_pixel_q25_v1\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 22264.098, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.0}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"chirps_spi__modis_ndvi\": null, \"modis_ndvi__era5_soil_moisture\": null}, \"pairwise_iou\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.707764}, \"pairwise_pct_delta\": {\"chirps_spi__era5_soil_moisture\": 200.0, \"chirps_spi__modis_ndvi\": 200.0, \"modis_ndvi__era5_soil_moisture\": 34.223}, \"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 719, "id": "multi_hazard/drought/000025/C", "dataset_id": "multi_hazard/drought/000025/C", "sample_id": "drought/000025", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "22c3ff882924a848d9856f632a4e3dd9170056378cac9eac9aa34a4199fff3ca"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"schools\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"schools\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `drought_anomaly_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"drought_anomaly_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload\": {\"aoi_id\": \"drought__000025\", \"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"ScoreHazard\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `schools` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"schools\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=82e8026e2017]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"schools\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"drought__000025\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"schools\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 0.0, \"footprint_handle\": \"low_chirps_spi\", \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 720, "id": "multi_hazard/drought/000025/D", "dataset_id": "multi_hazard/drought/000025/D", "sample_id": "drought/000025", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "82e8026e20179c0db185619f4c360533580cc2940c1c021067b8358f5c97ca9c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 29 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 29 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 29 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 29 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"ndvi_z_score\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"ndvi_z_score\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the AOI-mean MODIS NDVI z-score for Horn of Africa drought AOI 29 on 2024-08-15, using finite pixels in the declared target raster and the six declared same-season historical baseline observations?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: MODIS NDVI target raster plus the declared six-observation seasonal baseline.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"anomaly_class\": \"positive\", \"aoi_id\": \"drought__000029\", \"baseline_mean\": 0.293038, \"baseline_std\": 0.040206, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.400623, \"z_score\": 2.67584}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=33c9a14c00a6]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"ndvi_z_score\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"anomaly_class\": \"positive\", \"aoi_id\": \"drought__000029\", \"baseline_mean\": 0.293038, \"baseline_std\": 0.040206, \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"target_value\": 0.400623, \"z_score\": 2.67584}, \"stats_1_payload\": {\"anomaly_class\": \"positive\", \"aoi_id\": \"drought__000029\", \"baseline_mean\": 0.293038, \"baseline_std\": 0.040206, \"baseline_years_used\": 6, \"method\": \"z_score\", \"metrics\": {\"ndvi_z_score\": true, \"vegetation_anomaly\": true}, \"modality\": \"modis_ndvi_anomaly\", \"n_baseline_observations\": 6, \"target_date\": \"2024-08-15\", \"tar...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 729, "id": "multi_hazard/drought/000029/A", "dataset_id": "multi_hazard/drought/000029/A", "sample_id": "drought/000029", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "33c9a14c00a61e8b0ee2b5b064fc3ffeb5e4771b96c94f7dd920830c592817f3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 29 on 2024-08-15?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 29 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 29 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `drought_anomaly` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"drought_anomaly\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 29 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `question_defined_class` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"question_defined_class\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the dominant train-relative CHIRPS SPI pixel quartile over Horn of Africa drought AOI 29 on 2024-08-15?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: CHIRPS same-window SPI target and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q3_mid_high_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q4_high_spi\": {\"area_ha\": 31462.873, \"mean_value\": 5.003912, \"pixel_count\": 16384}}...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ae4e4bc5f51f]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"question_defined_class\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q3_mid_high_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q4_high_spi\": {\"area_ha\": 31462.873, \"mean_value\": 5.003912, \"pixel_count\": 16384}}, \"dominant_class\": \"q4_high_spi\", \"max\": 7.749645, \"mean\": 5.003912, \"units\": \"z_score\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"drought__000029\", \"by_class\": {\"q1_low_spi\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}, \"q2_mid_low_spi\": {\"area_h...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 730, "id": "multi_hazard/drought/000029/B", "dataset_id": "multi_hazard/drought/000029/B", "sample_id": "drought/000029", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ae4e4bc5f51f7c5cb75fe9c5c39a510eaf1f6a67f455adc17b6c38dc7087f896"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - Train-only drought stress normalization and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"drought\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"drought\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"iou\\\", \\\"minimum_iou\\\": 0.3, \\\"threshold_contract\\\": \\\"per_source_train_pixel_q25_v1\\\", \\\"normalization_fit_split\\\": \\\"train\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"iou\",\"minimum_iou\":0.3,\"threshold_contract\":\"per_source_train_pixel_q25_v1\",\"normalization_fit_split\":\"train\"}}}}"}, {"role": "user", "content": "QUESTION\nDo the train-relative bottom-quartile CHIRPS SPI, MODIS NDVI, and ERA5 soil-moisture stress masks reach median pairwise IoU 0.30 for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three co-registered continuous source rasters used to derive train-relative low-state masks.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only drought stress normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"agreement_class\": \"low\", \"median_area_ha\": 7401.0, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.0}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"chirps_sp...\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f1d15fe881ea]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"iou\", \"minimum_iou\": 0.3, \"normalization_fit_split\": \"train\", \"sources\": \"all\", \"threshold_contract\": \"per_source_train_pixel_q25_v1\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"agreement_class\": \"low\", \"median_area_ha\": 7401.0, \"median_pct_delta\": 200.0, \"n_sources_used\": 3, \"pairwise_categorical_kappa\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.0}, \"pairwise_correlation\": {\"chirps_spi__era5_soil_moisture\": null, \"chirps_spi__modis_ndvi\": null, \"modis_ndvi__era5_soil_moisture\": null}, \"pairwise_iou\": {\"chirps_spi__era5_soil_moisture\": 0.0, \"chirps_spi__modis_ndvi\": 0.0, \"modis_ndvi__era5_soil_moisture\": 0.235229}, \"pairwise_pct_delta\": {\"chirps_spi__era5_soil_moisture\": 200.0, \"chirps_spi__modis_ndvi\": 200.0, \"modis_ndvi__era5_soil_moisture\": 123.826}, \"pa...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 731, "id": "multi_hazard/drought/000029/C", "dataset_id": "multi_hazard/drought/000029/C", "sample_id": "drought/000029", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f1d15fe881ea3543fa255819af27b967d1661dae711c98831798078eaeee01ff"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"drought\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"drought\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"schools\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"schools\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `drought_anomaly_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"drought_anomaly_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"drought_anomaly_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `drought` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"drought\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"drought\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload\": {\"aoi_id\": \"drought__000029\", \"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"ScoreHazard\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `schools` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"schools\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nHow many schools are inside the train-relative bottom-quartile CHIRPS SPI footprint for Horn of Africa drought AOI 29?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard drought / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard drought / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative bottom-quartile CHIRPS SPI footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"drought\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"low_chirps_spi\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=82e8026e2017]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"schools\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"drought__000029\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"schools\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}}, \"distance_method\": \"local_azimuthal_equidistant_cell_center_v1\", \"footprint_area_ha\": 0.0, \"footprint_handle\": \"low_chirps_spi\", \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 732, "id": "multi_hazard/drought/000029/D", "dataset_id": "multi_hazard/drought/000029/D", "sample_id": "drought/000029", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "82e8026e20179c0db185619f4c360533580cc2940c1c021067b8358f5c97ca9c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Santiago pollution episode AOI 16?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `pollution_peak` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"pollution_peak\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"peak_no2_column\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"peak_no2_column\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"air_pollution__000016\", \"handle\": \"s5p_no2__peak\", \"max\": 142.573257, \"mean\": 100.16854, \"median\": 97.306221, \"metrics\": {\"peak_no2_column\": true, \"pollution_peak\": true}, \"min\": 45.074989, \"modality\": \"sentinel5p_no2\", \"p95_no2\": 127.281219, \"peak_no2\": 142.573257, \"percentiles\": {\"p50\": 97.306221, \"p95...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4210f261ee57]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"peak_no2_column\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"air_pollution__000016\", \"handle\": \"s5p_no2__peak\", \"max\": 142.573257, \"mean\": 100.16854, \"median\": 97.306221, \"metrics\": {\"peak_no2_column\": true, \"pollution_peak\": true}, \"min\": 45.074989, \"modality\": \"sentinel5p_no2\", \"p95_no2\": 127.281219, \"peak_no2\": 142.573257, \"percentiles\": {\"p50\": 97.306221, \"p95\": 127.281219, \"p99\": 134.833282}, \"units\": \"umol_m2\"}, \"stats_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"air_pollution__000016\", \"handle\": \"s5p_no2__peak\", \"mask_applied\": null, \"mask_pixel_count\": null, \"max\": 142.573257, \"mean\": 100.16854, \"median\": 97.306221...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 769, "id": "multi_hazard/air_pollution/000016/A", "dataset_id": "multi_hazard/air_pollution/000016/A", "sample_id": "air_pollution/000016", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4210f261ee57abd084286b015898907672072302ebbc368e0e814478372934e2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 16?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `pollution_peak` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"pollution_peak\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `air_pollution` on `benchmark_relative_column_quartile` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"air_pollution\",\"scale\":\"benchmark_relative_column_quartile\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"q1_low\": {\"area_ha\": 6442.144, \"mean_value\": 83.025192, \"pixel_count\": 3992}, \"q2_mid_low\": {\"area_ha\": 17722.257, \"mean_value\": 102.822242, \"pixel_count\": 10984}, \"q3_mid_high\": {\"area_ha\": 2271.225, \"mean_value\": 128.071938, \"pixel_count\": 1408}, \"q4_high\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixe...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c157811e1d6]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"air_pollution\", \"question\": \"question_1\", \"scale\": \"benchmark_relative_column_quartile\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"q1_low\": {\"area_ha\": 6442.144, \"mean_value\": 83.025192, \"pixel_count\": 3992}, \"q2_mid_low\": {\"area_ha\": 17722.257, \"mean_value\": 102.822242, \"pixel_count\": 10984}, \"q3_mid_high\": {\"area_ha\": 2271.225, \"mean_value\": 128.071938, \"pixel_count\": 1408}, \"q4_high\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"q2_mid_low\", \"max\": 142.573257, \"mean\": 100.16854, \"units\": \"umol_m2\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"air_pollution__000016\", \"by_class\": {\"q1_low\": {\"area_ha\": 6442.144, \"mean_value\": 83.025192,...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 770, "id": "multi_hazard/air_pollution/000016/B", "dataset_id": "multi_hazard/air_pollution/000016/B", "sample_id": "air_pollution/000016", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9c157811e1d64733e14af9d1c0bec1030cdf8ee89510b0cb55ece52caced5bbb"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"air_pollution\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"air_pollution\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"benchmark_relative_intensity_agreement\\\", \\\"required_agreement_fraction\\\": 1.0, \\\"normalization_fit_split\\\": \\\"train\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"benchmark_relative_intensity_agreement\",\"required_agreement_fraction\":1.0,\"normalization_fit_split\":\"train\"}}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"n_sources_used\": 3, \"verdict\": \"sources_agree\"}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=23dab2fdc9f1]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"benchmark_relative_intensity_agreement\", \"normalization_fit_split\": \"train\", \"required_agreement_fraction\": 1.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"n_sources_used\": 3, \"verdict\": \"sources_agree\"}, \"compare_1_payload\": {\"agree\": true, \"agreement_fraction\": 1.0, \"aoi_id\": \"air_pollution__000016\", \"metric\": \"benchmark_relative_intensity_agreement\", \"n_sources_used\": 3, \"normalization_fit_split\": \"train\", \"normalization_train_sample_ids\": [\"000001\", \"000002\", \"000005\", \"000006\", \"000009\", \"000010\", \"000013\", \"000014\", \"000017\", \"000018\", \"000020\", \"000021\", \"000022\", \"000024\", \"000026\", \"000028\", \"000029\", \"000030\"], \"required_agreement_fraction\": 1.0, \"source_intensity_states\": {\"cams_no2\": \"below_training_typical\", \"modis_aod\": \"below_training_typical\", \"s5p_no2\": \"below_training_typical\"}, \"sourc...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 771, "id": "multi_hazard/air_pollution/000016/C", "dataset_id": "multi_hazard/air_pollution/000016/C", "sample_id": "air_pollution/000016", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "23dab2fdc9f15860b52ae9ea5ab2fdd4516354dad8cca7c4e167ecc926a24a7e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"population\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"population\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `pollution_peak_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"pollution_peak_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `air_pollution` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"air_pollution\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"air_pollution\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}, \"score_1_payload\": {\"aoi_id\": \"air_pollution__000016\", \"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"Sco...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `high_no2_column_population` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"high_no2_column_population\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Santiago pollution episode AOI 16?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"hospitals\"}, {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d00a9bc77cf8]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"high_no2_column_population\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"hospitals\"}, {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"air_pollution__000016\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"hospitals\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}, \"schools\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 772, "id": "multi_hazard/air_pollution/000016/D", "dataset_id": "multi_hazard/air_pollution/000016/D", "sample_id": "air_pollution/000016", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d00a9bc77cf860f5bb1dbc8457d26763c0af04ed14c179466fae220e70b19046"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 25?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `pollution_peak` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"pollution_peak\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"peak_no2_column\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"metrics\":{\"peak_no2_column\":true},\"region\":\"aoi\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the peak Sentinel-5P NO2 column during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / primary scalar or area statistic task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / primary scalar or area statistic: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-wise per-pixel peak tropospheric NO2 raster.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"aoi_id\": \"air_pollution__000025\", \"handle\": \"s5p_no2__peak\", \"max\": 133.512955, \"mean\": 95.276972, \"median\": 94.316376, \"metrics\": {\"peak_no2_column\": true, \"pollution_peak\": true}, \"min\": 68.151085, \"modality\": \"sentinel5p_no2\", \"p95_no2\": 115.016388, \"peak_no2\": 133.512955, \"percentiles\": {\"p50\": 94.316376, \"p95...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=29e9bd1b28e0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"peak_no2_column\": true}, \"question\": \"question_1\", \"region\": \"aoi\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"aoi_id\": \"air_pollution__000025\", \"handle\": \"s5p_no2__peak\", \"max\": 133.512955, \"mean\": 95.276972, \"median\": 94.316376, \"metrics\": {\"peak_no2_column\": true, \"pollution_peak\": true}, \"min\": 68.151085, \"modality\": \"sentinel5p_no2\", \"p95_no2\": 115.016388, \"peak_no2\": 133.512955, \"percentiles\": {\"p50\": 94.316376, \"p95\": 115.016388, \"p99\": 129.749496}, \"units\": \"umol_m2\"}, \"stats_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"air_pollution__000025\", \"handle\": \"s5p_no2__peak\", \"mask_applied\": null, \"mask_pixel_count\": null, \"max\": 133.512955, \"mean\": 95.276972, \"median\": 94.316376...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 797, "id": "multi_hazard/air_pollution/000025/A", "dataset_id": "multi_hazard/air_pollution/000025/A", "sample_id": "air_pollution/000025", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "29e9bd1b28e0a382e4186ae2ff3b649e8a3e9d5e0de13aad6b84c280043d7c51"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 25?\n\nREGISTERED INPUTS\n- hazard_stack_1 [hazard_stack] - Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\"} into a `pollution_peak` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\"},\"mode\":\"pollution_peak\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `air_pollution` on `benchmark_relative_column_quartile` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"air_pollution\",\"scale\":\"benchmark_relative_column_quartile\"}}}"}, {"role": "user", "content": "QUESTION\nWhat is the training-relative S5P NO2-column quartile class at the peak for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / dominant severity or class decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: prepare reusable evidence from hazard_stack_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / dominant severity or class decision: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Sentinel-5P episode-peak NO2 raster and train-only quartile contract.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"by_class\": {\"q1_low\": {\"area_ha\": 11282.912, \"mean_value\": 82.521696, \"pixel_count\": 6652}, \"q2_mid_low\": {\"area_ha\": 15657.71, \"mean_value\": 102.688443, \"pixel_count\": 9236}, \"q3_mid_high\": {\"area_ha\": 840.568, \"mean_value\": 128.332899, \"pixel_count\": 496}, \"q4_high\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_c...\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=168196a8581c]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"air_pollution\", \"question\": \"question_1\", \"scale\": \"benchmark_relative_column_quartile\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"by_class\": {\"q1_low\": {\"area_ha\": 11282.912, \"mean_value\": 82.521696, \"pixel_count\": 6652}, \"q2_mid_low\": {\"area_ha\": 15657.71, \"mean_value\": 102.688443, \"pixel_count\": 9236}, \"q3_mid_high\": {\"area_ha\": 840.568, \"mean_value\": 128.332899, \"pixel_count\": 496}, \"q4_high\": {\"area_ha\": 0.0, \"mean_value\": null, \"pixel_count\": 0}}, \"dominant_class\": \"q2_mid_low\", \"max\": 133.512955, \"mean\": 95.276972, \"units\": \"umol_m2\"}, \"score_1_payload\": {\"analysis_scope\": \"whole_aoi\", \"aoi_id\": \"air_pollution__000025\", \"by_class\": {\"q1_low\": {\"area_ha\": 11282.912, \"mean_value\": 82.521696, \"p...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 798, "id": "multi_hazard/air_pollution/000025/B", "dataset_id": "multi_hazard/air_pollution/000025/B", "sample_id": "air_pollution/000025", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "168196a8581c65eb45d46d710656eea5fcb58a564f17eade0f0bb985a33d4fed"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\nREGISTERED INPUTS\n- agreement_sources_1 [agreement_sources] - Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"agreement_sources_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `agreement_sources_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"agreement_sources_1\",\"role\":\"air_pollution\",\"options\":{\"sources\":\"all\"}}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"agreement_sources_1\", \"options\": {\"sources\": \"all\"}, \"question\": \"question_1\", \"role\": \"air_pollution\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"sources\\\": \\\"scene_2\\\"} into a `multisource_hazard` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"sources\":\"scene_2\"},\"mode\":\"multisource_hazard\"}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"hazard\": \"scene_1\", \"sources\": \"scene_2\"}, \"mode\": \"multisource_hazard\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"sources\\\": \\\"all\\\", \\\"criterion\\\": \\\"benchmark_relative_intensity_agreement\\\", \\\"required_agreement_fraction\\\": 1.0, \\\"normalization_fit_split\\\": \\\"train\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"compare\":{\"sources\":\"all\",\"criterion\":\"benchmark_relative_intensity_agreement\",\"required_agreement_fraction\":1.0,\"normalization_fit_split\":\"train\"}}}}"}, {"role": "user", "content": "QUESTION\nDo Sentinel-5P NO2, CAMS NO2, and MODIS AOD agree on the training-relative episode-intensity state for Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / cross-source agreement decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"agreement_sources_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: prepare reusable evidence from hazard_stack_1, agreement_sources_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / cross-source agreement decision: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- agreement_sources_1 [agreement_sources] - agreement_sources_1: Three source rasters whose AOI means are standardized within source against the training AOIs.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Train-only cross-product intensity normalization and handle inventory.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"n_sources_used\": 3, \"verdict\": \"sources_disagree\"}\n- compare_1_summary = \"compared sources `compare_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c1031bdf7843]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"benchmark_relative_intensity_agreement\", \"normalization_fit_split\": \"train\", \"required_agreement_fraction\": 1.0, \"sources\": \"all\"}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"n_sources_used\": 3, \"verdict\": \"sources_disagree\"}, \"compare_1_payload\": {\"agree\": false, \"agreement_fraction\": 0.333333, \"aoi_id\": \"air_pollution__000025\", \"metric\": \"benchmark_relative_intensity_agreement\", \"n_sources_used\": 3, \"normalization_fit_split\": \"train\", \"normalization_train_sample_ids\": [\"000001\", \"000002\", \"000005\", \"000006\", \"000009\", \"000010\", \"000013\", \"000014\", \"000017\", \"000018\", \"000020\", \"000021\", \"000022\", \"000024\", \"000026\", \"000028\", \"000029\", \"000030\"], \"required_agreement_fraction\": 1.0, \"source_intensity_states\": {\"cams_no2\": \"training_typical\", \"modis_aod\": \"above_training_typical\", \"s5p_no2\": \"below_training_typical\"}, \"so...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 799, "id": "multi_hazard/air_pollution/000025/C", "dataset_id": "multi_hazard/air_pollution/000025/C", "sample_id": "air_pollution/000025", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "CompareSources"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c1031bdf784398856c5fda71da4f77fd3c5bc776e1a6e2d96c393df8fc168702"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\nREGISTERED INPUTS\n- exposure_layers_1 [exposure_layers] - Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"hazard_stack_1\",\"exposure_layers_1\"]},{\"id\":\"s2\",\"objective\":\"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `hazard_stack_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"hazard_stack_1\",\"role\":\"air_pollution\",\"time\":\"event\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"hazard_stack_1\", \"question\": \"question_1\", \"role\": \"air_pollution\", \"time\": \"event\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `exposure_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"exposure_layers_1\",\"role\":\"population\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"exposure_layers_1\", \"question\": \"question_1\", \"role\": \"population\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"hazard\\\": \\\"scene_1\\\", \\\"exposure\\\": \\\"scene_2\\\"} into a `pollution_peak_exposure` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"hazard\":\"scene_1\",\"exposure\":\"scene_2\"},\"mode\":\"pollution_peak_exposure\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"exposure\": \"scene_2\", \"hazard\": \"scene_1\"}, \"mode\": \"pollution_peak_exposure\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `air_pollution` on `impact_footprint` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"hazard\":\"air_pollution\",\"scale\":\"impact_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"air_pollution\", \"question\": \"question_1\", \"scale\": \"impact_footprint\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}, \"score_1_payload\": {\"aoi_id\": \"air_pollution__000025\", \"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}, \"score_1_payload_keys\": [\"aoi_id\", \"hazard\", \"impact_footprint\"], \"score_1_summary\": \"scored hazard `score_1`\"}, \"layers\": []}, \"text\": \"scored hazard `score_1`\", \"tool\": \"Sco...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `high_no2_column_population` exposure with metrics {\\\"summary\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"score_1\",\"exposure\":\"high_no2_column_population\",\"metrics\":{\"summary\":true}}}}"}, {"role": "user", "content": "QUESTION\nWhat population is located within the training-defined top-quartile S5P NO2-column footprint during Delhi pollution episode AOI 25?\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the multi-hazard air pollution / exposure, nearest-asset, or population impact decision task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"hazard_stack_1\", \"exposure_layers_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: prepare reusable evidence from hazard_stack_1, exposure_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"multi-hazard air pollution / exposure, nearest-asset, or population impact decision: estimate the requested exposure outcome from prepared hazard and asset evidence. Report the relevant counts, distances, population/asset support, and hazard class.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- exposure_layers_1 [exposure_layers] - exposure_layers_1: Co-registered WorldPop 2020 grid and authoritative cached OSM hospital/school geometries.\n- hazard_stack_1 [hazard_stack] - hazard_stack_1: Binary train-relative top-quartile Sentinel-5P NO2-column footprint.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"hospitals\"}, {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020...\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- score_1_evidence = {\"hazard\": \"air_pollution\", \"impact_footprint\": {\"area_ha\": 0.0, \"area_method\": \"grid_aware_wgs84_ellipsoidal_or_projected_affine_v1\", \"handle\": \"high_no2_column\", \"pixel_count\": 0}}\n- score_1_summary = \"scored hazard `score_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d00a9bc77cf8]\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"high_no2_column_population\", \"metrics\": {\"summary\": true}, \"question\": \"question_1\", \"work\": \"score_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"critical_infrastructure\": [{\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"hospitals\"}, {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest_distance_m\": null, \"nearest_name\": null, \"type\": \"schools\"}], \"footprint_area_ha\": 0.0, \"population_source\": \"WorldPop/GP/100m/pop 2020 population-count clip\", \"population_within_buffer\": 0.0, \"population_within_footprint\": 0.0}, \"exposure_1_payload\": {\"aoi_id\": \"air_pollution__000025\", \"asset_geometry_contract\": \"cached_feature_representative_point_v1\", \"critical_infrastructure\": {\"hospitals\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null, \"name\": null}}, \"schools\": {\"in_buffer\": 0, \"in_footprint\": 0, \"nearest\": {\"distance_m\": null,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "data/multi_hazard", "export_source": "geodisaster_test", "idx": 800, "id": "multi_hazard/air_pollution/000025/D", "dataset_id": "multi_hazard/air_pollution/000025/D", "sample_id": "air_pollution/000025", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ScoreHazard", "EstimateExposure"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d00a9bc77cf860f5bb1dbc8457d26763c0af04ed14c179466fae220e70b19046"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000016\", \"severe_count\": 0, \"severe_footprint...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d113b7b1022a]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000016\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 41, \"total_footprint_m2\": 1474194.2331}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.146662, \"scene_id\": \"hurricane-harvey_0000...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000016\", \"severe_count\": 0, \"severe_footprint...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d113b7b1022a]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 41, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000016\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 41, \"total_footprint_m...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 833, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q1", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d113b7b1022ab886eeb780abe75cdcf73261e4cce0279b50d9293320974f14cd"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"unclassified\": 0}, \"total_area_m2\": 839314.6476, \"unclassified\": 0}, {\"building_count\": 29, \"cell_id\": \"Q1\", \"classified_building_count\": 29, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 29, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"seve...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"unclassified\": 0}, \"total_area_m2\": 839314.6476, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\": [...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 834, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q2", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"unclassified\": 0}, \"total_area_m2\": 89603.2179, \"unclassified\": 0}, {\"building_count\": 22, \"cell_id\": \"G02\", \"classified_building_count\": 22, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"su...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"row\": 0, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=583e89df12e6]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 11, \"unclassified\": 0}, \"total_area_m2\": 89603.2179, \"unclassified\": 0}, {\"building_count\": 22, \"cell_id\": \"G02\", \"clas...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 835, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q3", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "583e89df12e61cea8afd30b58862803fecb12249bfe8d6e01e258e360deaa2e8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1fc18b681a4b]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1474194.2331}, \"damage_summary\": {\"classified_buildings\": 41, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 41, \"unclassified\": 0}, \"damaged_pct\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 41}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.146662, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_a...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1fc18b681a4b]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1474194.2331}, \"damage_summary\": {\"classi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 836, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q8", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1fc18b681a4b912758275bf98afb9a36aa46930e9a009f86ea890ebca366ff6d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000292\", \"severe_count\": 0, \"severe_footprint...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2cf75f3697c5]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000292\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 82, \"total_footprint_m2\": 1182037.7664}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harvey_0000...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000292\", \"severe_count\": 0, \"severe_footprint...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2cf75f3697c5]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 82, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000292\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 82, \"total_footprint_m...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 837, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q1", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2cf75f3697c588b5889fa28174036a6391924104cb41fa51a5f3e0f2d40f8b26"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 95558.0215, \"unclassified\": 0}, {\"building_count\": 46, \"cell_id\": \"Q1\", \"classified_building_count\": 46, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 46, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"se...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 95558.0215, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\": [{\"bui...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 838, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q2", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G00\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G00\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 11905.606, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"G01\", \"classified_building_count\": 8, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_hi...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G00\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G00\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2849a5e297e9]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G00\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 11905.606, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"G01\", \"classified...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 839, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q3", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2849a5e297e957409a7786403f2cf0b0b14e1db398de338276764f19949c2db2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=574e4f3841df]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1182037.7664}, \"damage_summary\": {\"classified_buildings\": 82, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 82, \"unclassified\": 0}, \"damaged_pct\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 82}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_a...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=574e4f3841df]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1182037.7664}, \"damage_summary\": {\"classi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 840, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q8", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "574e4f3841df3fa1da1df87d243558ffd096ad930fbf590621b0f9f72bf074c0"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000366\", \"severe_count\": 0, \"severe_footpri...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=72761f9f80bd]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000366\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 156, \"total_footprint_m2\": 2420761.2124}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harvey...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000366\", \"severe_count\": 0, \"severe_footpri...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=72761f9f80bd]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 156, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000366\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 156, \"total_footprin...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 881, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q1", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "72761f9f80bd5e2698d6ce1800cf10da9896867b6ed8b76632e9fae2a00de600"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"unclassified\": 0}, \"total_area_m2\": 686788.3227, \"unclassified\": 0}, {\"building_count\": 55, \"cell_id\": \"Q1\", \"classified_building_count\": 55, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 55, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"seve...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"unclassified\": 0}, \"total_area_m2\": 686788.3227, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\": [...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 882, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q2", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 15, \"cell_id\": \"G00\", \"classified_building_count\": 15, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 15, \"cell_id\": \"G00\", \"classified_building_count\": 15, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"unclassified\": 0}, \"total_area_m2\": 141322.1241, \"unclassified\": 0}, {\"building_count\": 16, \"cell_id\": \"G01\", \"classified_building_count\": 16, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"s...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 15, \"cell_id\": \"G00\", \"classified_building_count\": 15, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"row\": 0, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 15, \"cell_id\": \"G00\", \"classified_building_count\": 15, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d22950da5d60]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 15, \"cell_id\": \"G00\", \"classified_building_count\": 15, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 15, \"unclassified\": 0}, \"total_area_m2\": 141322.1241, \"unclassified\": 0}, {\"building_count\": 16, \"cell_id\": \"G01\", \"cla...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 883, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q3", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d22950da5d60520dce4b8b7e9b2f4240e32322cb612074f363b570048d3c99df"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5468fcfb55df]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 2420761.2124}, \"damage_summary\": {\"classified_buildings\": 156, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 156, \"unclassified\": 0}, \"damaged_pct\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 156}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mas...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5468fcfb55df]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 2420761.2124}, \"damage_summary\": {\"classi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 884, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q8", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5468fcfb55df9c6f4275eca7e095ca6345a8977d2f84c68d7ca2dc535a567d8d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_count\": 12, \"damaged_pct\": 15.19, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000217\", \"severe_count\": 0, \"severe_foo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=68a36d601e87]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_count\": 12, \"damaged_pct\": 15.19, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000217\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 79, \"total_footprint_m2\": 588862.848}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_count\": 12, \"damaged_pct\": 15.19, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"scene_id\": \"hurricane-michae...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"d...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_count\": 12, \"damaged_pct\": 15.19, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000217\", \"severe_count\": 0, \"severe_foo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=68a36d601e87]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 79, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_count\": 12, \"damaged_pct\": 15.19, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000217\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 79, \"total_footp...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 901, "id": "xbd_damage/hurricane-michael_00000217/XBD-Q1", "dataset_id": "xbd_damage/hurricane-michael_00000217/XBD-Q1", "sample_id": "hurricane-michael_00000217", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "68a36d601e87259b6d2c4e16741e538bcab1e61d3d7ce4e9ea0550f58f3ef3d4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pc...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.060606, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 81592.8696, \"unclassified\": 0}, {\"building_count\": 30, \"cell_id\": \"Q1\", \"classified_building_count\": 30, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 28, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"sever...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pc...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.060606, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 81592.8696, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 902, "id": "xbd_damage/hurricane-michael_00000217/XBD-Q2", "dataset_id": "xbd_damage/hurricane-michael_00000217/XBD-Q2", "sample_id": "hurricane-michael_00000217", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 6, \"cell_id\": \"G30\", \"classified_building_count\": 6, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 6, \"cell_id\": \"G30\", \"classified_building_count\": 6, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.222222, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"unclassified\": 0}, \"total_area_m2\": 40314.7938, \"unclassified\": 0}, {\"building_count\": 5, \"cell_id\": \"G33\", \"classified_building_count\": 5, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 3, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.13333...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 6, \"cell_id\": \"G30\", \"classified_building_count\": 6, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 3, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 6, \"cell_id\": \"G30\", \"classified_building_count\": 6, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7a4feda8b947]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 6, \"cell_id\": \"G30\", \"classified_building_count\": 6, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.222222, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"unclassified\": 0}, \"total_area_m2\": 40314.7938, \"unclassified\": 0}, {\"building_count\": 5, \"cell_id\": \"G33\", \"c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 903, "id": "xbd_damage/hurricane-michael_00000217/XBD-Q3", "dataset_id": "xbd_damage/hurricane-michael_00000217/XBD-Q3", "sample_id": "hurricane-michael_00000217", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7a4feda8b94799bf570f0226fd8b7839e118ecffed909d499c411f985d304ab8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e7117a8188ff]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 588862.848}, \"damage_summary\": {\"classified_buildings\": 79, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 12, \"no-damage\": 67, \"unclassified\": 0}, \"damaged_pct\": 15.19, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 79}, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_av...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e7117a8188ff]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 588862.848}, \"damage_summary\": {\"classif...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 904, "id": "xbd_damage/hurricane-michael_00000217/XBD-Q8", "dataset_id": "xbd_damage/hurricane-michael_00000217/XBD-Q8", "sample_id": "hurricane-michael_00000217", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e7117a8188ff74c9dae960e758b3e7d643475886b67f13c53951ff21b8c2e98a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e43a408c2b8]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footprint_m2\": 241740.9646, \"severe_pct\": 85.19, \"total_buildings\": 33, \"total_footprint_m2\": 324524.8312}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.146662, \"scene_id\": \"hur...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e43a408c2b8]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footprint_m2\": 241740.9646, \"severe_pct\": 85.19, \"total_buildings\": 33, \"t...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 949, "id": "xbd_damage/hurricane-harvey_00000081/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000081/XBD-Q1", "sample_id": "hurricane-harvey_00000081", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0e43a408c2b8a1be88f8cef78b069e3526ef4278ee21445a2204ea40a1aa08b6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 91724.3313, \"severe_count\": 8,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 91724.3313, \"severe_count\": 8, \"severe_pct\": 72.7273, \"severity_score\": 0.575758, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 130573.1209, \"unclassified\": 0}, {\"building_count\": 13, \"cell_id\": \"Q2\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 86474.0058, \"severe_cou...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 91724.33...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 91724.3313, \"severe_count\": 8,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ee5882af5a6a]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"Q0\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 91724.3313, \"severe_count\": 8, \"severe_pct\": 72.7273, \"severity_score\": 0.575758, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 8, \"minor_damage\": 3, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 130573.1209, \"unclassified\": 0}]}, \"rank_1_payload\": {\"a...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 950, "id": "xbd_damage/hurricane-harvey_00000081/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000081/XBD-Q2", "sample_id": "hurricane-harvey_00000081", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ee5882af5a6af7263359c32c7de0ca21f44b60d1ae4e462fc759507c7505604e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G01\", \"classified_building_count\": 1, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 10093.8573, \"severe_count\": 1, \"sev...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G01\", \"classified_building_count\": 1, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 10093.8573, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 10093.8573, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G10\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 2, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 24163.3295, \"severe_count\": 2, \"severe_pct\": 100.0, \"sev...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G01\", \"classified_building_count\": 1, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 10093.8573,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G01\", \"classified_building_count\": 1, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 10093.8573, \"severe_count\": 1, \"sev...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b71df4575f86]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G01\", \"classified_building_count\": 1, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 10093.8573, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 10093.8573, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 951, "id": "xbd_damage/hurricane-harvey_00000081/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000081/XBD-Q3", "sample_id": "hurricane-harvey_00000081", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b71df4575f86d2f3b637a8dd498a51703b61726acaffd165e9de5c09e4090c42"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ee4c1d71a6aa]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 241740.9646, \"total_footprint_m2\": 324524.8312}, \"damage_summary\": {\"classified_buildings\": 27, \"counts\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_pct\": 100.0, \"severe_count\": 23, \"severe_pct\": 85.19, \"total_buildings\": 33}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.146662, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destr...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ee4c1d71a6aa]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 241740.9646, \"total_footprint_m2\": 324524.8312}, \"damage_summary\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 954, "id": "xbd_damage/hurricane-harvey_00000081/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000081/XBD-Q8", "sample_id": "hurricane-harvey_00000081", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ee4c1d71a6aa97aba9fe63801836634366456028e05f16ae1ebbb4532702680c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1000, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q5", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"unclassified\": 0}, \"total_area_m2\": 686788.3227, \"unclassified\": 0}, {\"building_count\": 55, \"cell_id\": \"Q1\", \"classified_building_count\": 55, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 55, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"seve...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"unclassified\": 0}, \"total_area_m2\": 686788.3227, \"unclassified\": 0}, {\"building_count\": 55, \"cell_id\": \"Q1\", \"classifie...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 54, \"cell_id\": \"Q0\", \"classified_building_count\": 54, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 54, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1001, "id": "xbd_damage/hurricane-harvey_00000366/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000366/XBD-Q7", "sample_id": "hurricane-harvey_00000366", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1012, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q5", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.101449, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"unclassified\": 0}, \"total_area_m2\": 150432.1751, \"unclassified\": 0}, {\"building_count\": 14, \"cell_id\": \"Q1\", \"classified_building_count\": 14, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0,...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.101449, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"unclassified\": 0}, \"total_area_m2\": 150432.1751, \"unclassified\": 0}, {\"building_count\": 14, \"cell_id\": \"Q1\", \"clas...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1013, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q7", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 38.89, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000548\", \"severe_count\": 1, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=460baea6fc49]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 38.89, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000548\", \"severe_count\": 1, \"severe_footprint_m2\": 421116.2196, \"severe_pct\": 5.56, \"total_buildings\": 18, \"total_footprint_m2\": 767586.5247}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 38.89, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"scene_id\": \"hurrican...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 38.89, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000548\", \"severe_count\": 1, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=460baea6fc49]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 18, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 38.89, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000548\", \"severe_count\": 1, \"severe_footprint_m2\": 421116.2196, \"severe_pct\": 5.56, \"total_buildings\": 18, \"to...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1021, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q1", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q1", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "460baea6fc49143d40bbd79c31ab225462aebed5b9cbaf30b76397690a90582d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 259154.8967, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"Q2\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 2, \"row\": 1, \"severe_area_m2\": 162872.7231, \"severe_count\": 0,...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.496...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0b94638e46ad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 259154.8967, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1022, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q2", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q2", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0b94638e46adeb73fbc93f573da1c2a8e38f9f8406c4d533fc0b6be67d32e6aa"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G10\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 127276.0539, \"severe_count\": 1, \"se...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G10\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 127276.0539, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 127276.0539, \"unclassified\": 0}, {\"building_count\": 3, \"cell_id\": \"G32\", \"classified_building_count\": 3, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_s...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G10\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 127276.0539,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G10\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 127276.0539, \"severe_count\": 1, \"se...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1d567b8277de]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G10\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 127276.0539, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 127276.0539, \"unclassified\": 0}, {\"building_count\": 3, \"cell_id\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1023, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q3", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q3", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1d567b8277defaa1844d959117896438719deacecce319770b75cc5df4694333"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c31b9d2b0439]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 421116.2196, \"total_footprint_m2\": 767586.5247}, \"damage_summary\": {\"classified_buildings\": 18, \"counts\": {\"destroyed\": 0, \"major-damage\": 1, \"minor-damage\": 6, \"no-damage\": 11, \"unclassified\": 0}, \"damaged_pct\": 38.89, \"severe_count\": 1, \"severe_pct\": 5.56, \"total_buildings\": 18}, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c31b9d2b0439]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 421116.2196, \"total_footprint_m2\": 767586.5247}, \"damage_summary\"...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1026, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q8", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q8", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c31b9d2b0439fdb1e76030a8990d926b639d20ffd65bf299f606ccdbe66e090f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1036, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q5", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 5971.6719, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"Q1\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 3, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 5971.6719, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"Q1\", \"classified_build...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1037, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q7", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_count\": 108, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000351\", \"severe_count\": 61, \"severe_fo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=97f60ef5896c]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_count\": 108, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000351\", \"severe_count\": 61, \"severe_footprint_m2\": 755352.0537, \"severe_pct\": 56.48, \"total_buildings\": 111, \"total_footprint_m2\": 1361531.3412}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_count\": 108, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_i...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_count\": 108, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000351\", \"severe_count\": 61, \"severe_fo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=97f60ef5896c]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 108, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_count\": 108, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000351\", \"severe_count\": 61, \"severe_footprint_m2\": 755352.0537, \"severe_pct\": 56.48, \"total_buildings\": 111...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1069, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q1", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "97f60ef5896c7b2cd9d02e07837be8bd8002878d3938a4dbb146f56bc08a5a9b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\": 34, \"severe_pct\": 73.913, \"severity_score\": 0.57971, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 539080.6625, \"unclassified\": 0}, {\"building_count\": 57, \"cell_id\": \"Q1\", \"classified_building_count\": 54, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 22, \"minor_damage\": 32, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 282170.0329, \"se...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bd158d5d12dc]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\": 34, \"severe_pct\": 73.913, \"severity_score\": 0.57971, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 539080.6625, \"unclassified\": 0}]}, \"rank_1_payload\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1070, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q2", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "bd158d5d12dce11f350452d714359f2ab629a01bba7e8e6923c77bcd26761f17"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G00\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 141253.1707, \"severe_count\": 11,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G00\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 141253.1707, \"severe_count\": 11, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 141928.2081, \"unclassified\": 0}, {\"building_count\": 11, \"cell_id\": \"G01\", \"classified_building_count\": 11, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 131320.3871, \"severe_count\": 11, \"severe_pct\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"G00\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 141253.17...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 11, \"cell_id\": \"G00\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 141253.1707, \"severe_count\": 11,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=eee2a351ad6a]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 11, \"cell_id\": \"G00\", \"classified_building_count\": 11, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 141253.1707, \"severe_count\": 11, \"severe_pct\": 100.0, \"severity_score\": 0.666667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 11, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 141928.2081, \"unclassified\": 0}, {\"building_count\": 11, \"ce...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1071, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q3", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "eee2a351ad6a2f7193f11f9ff6d3d81807054b60d5d6a30931da0bdbfa529ab2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=08e5a4b34dfb]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 755352.0537, \"total_footprint_m2\": 1361531.3412}, \"damage_summary\": {\"classified_buildings\": 108, \"counts\": {\"destroyed\": 0, \"major-damage\": 61, \"minor-damage\": 47, \"no-damage\": 0, \"unclassified\": 3}, \"damaged_pct\": 100.0, \"severe_count\": 61, \"severe_pct\": 56.48, \"total_buildings\": 111}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=d...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=08e5a4b34dfb]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 755352.0537, \"total_footprint_m2\": 1361531.3412}, \"damage_summary\"...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1074, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q8", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "08e5a4b34dfbb79205edb4258ec5f34b3d62599ced530e91b54032790b24aad6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1138, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q5", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"unclassified\": 0}, \"total_area_m2\": 965421.0393, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"Q1\", \"classified_building_count\": 8, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"unclassified\": 0}, \"total_area_m2\": 965421.0393, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"Q1\", \"classified...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1139, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q7", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1150, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q5", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 95558.0215, \"unclassified\": 0}, {\"building_count\": 46, \"cell_id\": \"Q1\", \"classified_building_count\": 46, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 46, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pc...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"unclassified\": 0}, \"total_area_m2\": 95558.0215, \"unclassified\": 0}, {\"building_count\": 46, \"cell_id\": \"Q1\", \"classified_bui...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 9, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 9, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1151, "id": "xbd_damage/hurricane-harvey_00000292/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000292/XBD-Q7", "sample_id": "hurricane-harvey_00000292", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_count\": 50, \"damaged_pct\": 51.55, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000162\", \"severe_count\": 32, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3ef60f626c85]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_count\": 50, \"damaged_pct\": 51.55, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000162\", \"severe_count\": 32, \"severe_footprint_m2\": 160816.4999, \"severe_pct\": 32.99, \"total_buildings\": 97, \"total_footprint_m2\": 369687.4153}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_count\": 50, \"damaged_pct\": 51.55, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"scene_id\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0},...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_count\": 50, \"damaged_pct\": 51.55, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000162\", \"severe_count\": 32, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3ef60f626c85]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 97, \"damage_distribution\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_count\": 50, \"damaged_pct\": 51.55, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000162\", \"severe_count\": 32, \"severe_footprint_m2\": 160816.4999, \"severe_pct\": 32.99, \"total_buildings\": 9...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1243, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q1", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q1", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3ef60f626c85381dd6a4dcedc3c6c0b7a803b5141e4693bee902b8d448ea1bd9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"severe_pct\": 47.619, \"severity_score\": 0.492063, \"subtype_histogram\": {\"destroyed\": 6, \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 123089.0598, \"unclassified\": 0}, {\"building_count\": 33, \"cell_id\": \"Q1\", \"classified_building_count\": 33, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 9, \"minor_damage\": 4, \"no_damage\": 20, \"row\": 0, \"severe_area_m2\": 31064.7134, \"severe_count\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a8e37a6ecbb5]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"severe_pct\": 47.619, \"severity_score\": 0.492063, \"subtype_histogram\": {\"destroyed\": 6, \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 123089.0598, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1244, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q2", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q2", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a8e37a6ecbb5d1c8a078f155ab06c0fd32b892c955fa135f432c7a703897be1e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G30\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 1977.5289, \"severe_count\": 1, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G30\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 1977.5289, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 1977.5289, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"G00\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 2, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 82813.7844, \"severe_count\": 4, \"severe_pct\": 100.0, \"severity_score\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G30\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 1977.5289, \"sev...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G30\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 1977.5289, \"severe_count\": 1, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2edeacc46072]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G30\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 1977.5289, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 1977.5289, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"G00\", \"cl...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1245, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q3", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q3", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2edeacc46072dccef57f15d49f810de7ad9c98fe93579e9db99a4571316d3c10"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7bf692836b12]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 160816.4999, \"total_footprint_m2\": 369687.4153}, \"damage_summary\": {\"classified_buildings\": 97, \"counts\": {\"destroyed\": 11, \"major-damage\": 21, \"minor-damage\": 18, \"no-damage\": 47, \"unclassified\": 0}, \"damaged_pct\": 51.55, \"severe_count\": 32, \"severe_pct\": 32.99, \"total_buildings\": 97}, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=dest...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7bf692836b12]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 160816.4999, \"total_footprint_m2\": 369687.4153}, \"damage_summary\"...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1248, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q8", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q8", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7bf692836b120cc755c652cd1a8b1ea56e045e920f5e013fcd4a821b7c43b7a8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1288, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q5", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.083333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 150454.2012, \"unclassified\": 0}, {\"building_count\": 6, \"cell_id\": \"Q1\", \"classified_building_count\": 6, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 6, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"sev...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.083333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 150454.2012, \"unclassified\": 0}, {\"building_count\": 6, \"cell_id\": \"Q1\", \"classifie...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1289, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q7", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_count\": 34, \"damaged_pct\": 34.34, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000077\", \"severe_count\": 4, \"severe_foo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c2ab6486774]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_count\": 34, \"damaged_pct\": 34.34, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000077\", \"severe_count\": 4, \"severe_footprint_m2\": 12763.6158, \"severe_pct\": 4.04, \"total_buildings\": 100, \"total_footprint_m2\": 389783.256}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_count\": 34, \"damaged_pct\": 34.34, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"scene_id\": \"hurri...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"d...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_count\": 34, \"damaged_pct\": 34.34, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000077\", \"severe_count\": 4, \"severe_foo...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c2ab6486774]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 99, \"damage_distribution\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_count\": 34, \"damaged_pct\": 34.34, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000077\", \"severe_count\": 4, \"severe_footprint_m2\": 12763.6158, \"severe_pct\": 4.04, \"total_buildings\": 100, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1309, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q1", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q1", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4c2ab64867747309bbcc7ff90e89de8722f3a26cf386d32b7109d68f0b46f700"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"severe_pct\": 15.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"unclassified\": 0}, \"total_area_m2\": 70729.9731, \"unclassified\": 0}, {\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 7, \"row\": 0, \"severe_area_m2\": 939.4572, \"severe_count\": 1, \"severe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2e4d3009d8cd]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"severe_pct\": 15.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"unclassified\": 0}, \"total_area_m2\": 70729.9731, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candid...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1310, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q2", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q2", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2e4d3009d8cdcd8f255c5f95a96e89dba1d2b5dc78b6fb04ce7d0c00132e89b3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G02\", \"classified_building_count\": 2, \"col\": 2, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 1758.6249, \"severe_count\": 2, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G02\", \"classified_building_count\": 2, \"col\": 2, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 1758.6249, \"severe_count\": 2, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 4003.5197, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G11\", \"classified_building_count\": 2, \"col\": 1, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 939.4572, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"G02\", \"classified_building_count\": 2, \"col\": 2, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 1758.6249, \"sev...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G02\", \"classified_building_count\": 2, \"col\": 2, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 1758.6249, \"severe_count\": 2, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=cccaed737f53]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"G02\", \"classified_building_count\": 2, \"col\": 2, \"destroyed\": 2, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 1758.6249, \"severe_count\": 2, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 4003.5197, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G11\", \"cl...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1311, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q3", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q3", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "cccaed737f53517cca8487278556d13d019e6528276d8395889591c8f8749604"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ed58c3fb9e94]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 12763.6158, \"total_footprint_m2\": 389783.256}, \"damage_summary\": {\"classified_buildings\": 99, \"counts\": {\"destroyed\": 3, \"major-damage\": 1, \"minor-damage\": 30, \"no-damage\": 65, \"unclassified\": 1}, \"damaged_pct\": 34.34, \"severe_count\": 4, \"severe_pct\": 4.04, \"total_buildings\": 100}, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ed58c3fb9e94]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 12763.6158, \"total_footprint_m2\": 389783.256}, \"damage_summary\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1314, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q8", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q8", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ed58c3fb9e94ddd3cfda377754767cc369158a78d64e4dadbb382b85cc03897c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"hurricane-harvey_00000081\", \"scenes\": [{\"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=00b575703a2b]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"hurricane-harvey_00000081\", \"scenes\": [{\"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footprint_m2\": 241740.9646, \"severe_pct\": 85.19, \"total_buildings\": 33}, {\"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000188\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"hurricane-harvey_00000081\", \"scenes\": [{\"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"hurricane-harvey_00000081\", \"scenes\": [{\"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=00b575703a2b]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"hurricane-harvey_00000081\", \"scenes\": [{\"classified_buildings\": 27, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 23, \"minor-damage\": 4, \"no-damage\": 0, \"unclassified\": 6}, \"damaged_count\": 27, \"damaged_pct\": 100.0, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000081\", \"severe_count\": 23, \"severe_footprint_m2\": 241740.9646, \"severe_pct\": 85.19, \"total_buildings\": 33}, {\"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1336, "id": "xbd_damage/hurricane-harvey_00000081/XBD-Q4", "dataset_id": "xbd_damage/hurricane-harvey_00000081/XBD-Q4", "sample_id": "hurricane-harvey_00000081", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "00b575703a2b979007a91192e5397da80e3671602a5bc03a3008137996b23934"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"joplin-tornado_00000075\", \"scenes\": [{\"classified_buildings\": 144, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 5, \"no-damage\": 139, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 3.47, \"disaster\": \"joplin-tornado\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bfa01da6a183]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"joplin-tornado_00000075\", \"scenes\": [{\"classified_buildings\": 144, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 5, \"no-damage\": 139, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 3.47, \"disaster\": \"joplin-tornado\", \"scene_id\": \"joplin-tornado_00000042\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 150}, {\"classified_buildings\": 74, \"damage_distribution\": {\"destroyed\": 45, \"major-damage\": 7, \"minor-damage\": 15, \"no-damage\": 7, \"unclassified\": 12}, \"damaged_count\": 67, \"damaged_pct\": 90.54, \"disaster\": \"joplin-tornado\", \"scene_id\": \"joplin-tornado_00000075\", \"severe_count\": 52, \"severe_footprint_m2\": 262303.9588, \"severe_pct\": 7...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"joplin-tornado_00000075\", \"scenes\": [{\"classified_buildings\": 144, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 5, \"no-damage\": 139, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 3.47, \"disaster\": \"joplin-torn...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"joplin-tornado_00000075\", \"scenes\": [{\"classified_buildings\": 144, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 5, \"no-damage\": 139, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 3.47, \"disaster\": \"joplin-tornado\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bfa01da6a183]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"joplin-tornado_00000075\", \"scenes\": [{\"classified_buildings\": 144, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 5, \"no-damage\": 139, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 3.47, \"disaster\": \"joplin-tornado\", \"scene_id\": \"joplin-tornado_00000042\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 150}, {\"classified_buildings\": 74, \"damage_distribution\": {\"destroyed\": 45, \"major-damage\": 7, \"minor-damage\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1343, "id": "xbd_damage/joplin-tornado_00000042/XBD-Q4", "dataset_id": "xbd_damage/joplin-tornado_00000042/XBD-Q4", "sample_id": "joplin-tornado_00000042", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "bfa01da6a1837b575f97cd2ba51993c6890d4fdb1187e8c1fef937a2acb54894"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"nepal-flooding_00000552\", \"scenes\": [{\"classified_buildings\": 180, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 176, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 2.22, \"disaster\": \"nepal-flooding\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be97ce2647d8]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"nepal-flooding_00000552\", \"scenes\": [{\"classified_buildings\": 180, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 176, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 2.22, \"disaster\": \"nepal-flooding\", \"scene_id\": \"nepal-flooding_00000267\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 180}, {\"classified_buildings\": 26, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 9, \"minor-damage\": 11, \"no-damage\": 6, \"unclassified\": 0}, \"damaged_count\": 20, \"damaged_pct\": 76.92, \"disaster\": \"nepal-flooding\", \"scene_id\": \"nepal-flooding_00000552\", \"severe_count\": 9, \"severe_footprint_m2\": 31881.6092, \"severe_pct\": 34.62...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"nepal-flooding_00000552\", \"scenes\": [{\"classified_buildings\": 180, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 176, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 2.22, \"disaster\": \"nepal-flood...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"nepal-flooding_00000552\", \"scenes\": [{\"classified_buildings\": 180, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 176, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 2.22, \"disaster\": \"nepal-flooding\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be97ce2647d8]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"nepal-flooding_00000552\", \"scenes\": [{\"classified_buildings\": 180, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 176, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 2.22, \"disaster\": \"nepal-flooding\", \"scene_id\": \"nepal-flooding_00000267\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 180}, {\"classified_buildings\": 26, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 9, \"minor-damage\": 1...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1355, "id": "xbd_damage/nepal-flooding_00000267/XBD-Q4", "dataset_id": "xbd_damage/nepal-flooding_00000267/XBD-Q4", "sample_id": "nepal-flooding_00000267", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "be97ce2647d899094fa287bec77bd3652dabc39fb6a5e1a6ed43bb81de2dabda"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"sunda-tsunami_00000023\", \"scenes\": [{\"classified_buildings\": 1195, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 3, \"minor-damage\": 0, \"no-damage\": 1192, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 0.25, \"disaster\": \"sunda-tsunami\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d32a57eb95bd]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"sunda-tsunami_00000023\", \"scenes\": [{\"classified_buildings\": 1195, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 3, \"minor-damage\": 0, \"no-damage\": 1192, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 0.25, \"disaster\": \"sunda-tsunami\", \"scene_id\": \"sunda-tsunami_00000023\", \"severe_count\": 3, \"severe_footprint_m2\": 6788.7146, \"severe_pct\": 0.25, \"total_buildings\": 1195}, {\"classified_buildings\": 10, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 5}, \"damaged_count\": 0, \"damaged_pct\": 0.0, \"disaster\": \"sunda-tsunami\", \"scene_id\": \"sunda-tsunami_00000119\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"tota...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"sunda-tsunami_00000023\", \"scenes\": [{\"classified_buildings\": 1195, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 3, \"minor-damage\": 0, \"no-damage\": 1192, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 0.25, \"disaster\": \"sunda-tsun...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"sunda-tsunami_00000023\", \"scenes\": [{\"classified_buildings\": 1195, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 3, \"minor-damage\": 0, \"no-damage\": 1192, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 0.25, \"disaster\": \"sunda-tsunami\", \"scene...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d32a57eb95bd]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"sunda-tsunami_00000023\", \"scenes\": [{\"classified_buildings\": 1195, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 3, \"minor-damage\": 0, \"no-damage\": 1192, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 0.25, \"disaster\": \"sunda-tsunami\", \"scene_id\": \"sunda-tsunami_00000023\", \"severe_count\": 3, \"severe_footprint_m2\": 6788.7146, \"severe_pct\": 0.25, \"total_buildings\": 1195}, {\"classified_buildings\": 10, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-d...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1370, "id": "xbd_damage/sunda-tsunami_00000023/XBD-Q4", "dataset_id": "xbd_damage/sunda-tsunami_00000023/XBD-Q4", "sample_id": "sunda-tsunami_00000023", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d32a57eb95bd46f9e959156921f9eabd2e6f6f8a5fb0560dc97a6768ea58e373"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"tuscaloosa-tornado_00000030\", \"scenes\": [{\"classified_buildings\": 67, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 1, \"no-damage\": 62, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 7.46, \"disaster\": \"tuscaloosa-tornado\",...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b4795580363]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"tuscaloosa-tornado_00000030\", \"scenes\": [{\"classified_buildings\": 67, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 1, \"no-damage\": 62, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 7.46, \"disaster\": \"tuscaloosa-tornado\", \"scene_id\": \"tuscaloosa-tornado_00000324\", \"severe_count\": 4, \"severe_footprint_m2\": 3005.1628, \"severe_pct\": 5.97, \"total_buildings\": 73}, {\"classified_buildings\": 121, \"damage_distribution\": {\"destroyed\": 12, \"major-damage\": 9, \"minor-damage\": 48, \"no-damage\": 52, \"unclassified\": 3}, \"damaged_count\": 69, \"damaged_pct\": 57.02, \"disaster\": \"tuscaloosa-tornado\", \"scene_id\": \"tuscaloosa-tornado_00000030\", \"severe_count\": 21, \"severe_footprint_m2\": 1...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"tuscaloosa-tornado_00000030\", \"scenes\": [{\"classified_buildings\": 67, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 1, \"no-damage\": 62, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 7.46, \"disaster\": \"tuscaloos...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"tuscaloosa-tornado_00000030\", \"scenes\": [{\"classified_buildings\": 67, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 1, \"no-damage\": 62, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 7.46, \"disaster\": \"tuscaloosa-tornado\",...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b4795580363]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"tuscaloosa-tornado_00000030\", \"scenes\": [{\"classified_buildings\": 67, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 1, \"no-damage\": 62, \"unclassified\": 6}, \"damaged_count\": 5, \"damaged_pct\": 7.46, \"disaster\": \"tuscaloosa-tornado\", \"scene_id\": \"tuscaloosa-tornado_00000324\", \"severe_count\": 4, \"severe_footprint_m2\": 3005.1628, \"severe_pct\": 5.97, \"total_buildings\": 73}, {\"classified_buildings\": 121, \"damage_distribution\": {\"destroyed\": 12, \"major-damage\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1372, "id": "xbd_damage/tuscaloosa-tornado_00000324/XBD-Q4", "dataset_id": "xbd_damage/tuscaloosa-tornado_00000324/XBD-Q4", "sample_id": "tuscaloosa-tornado_00000324", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8b4795580363cfb0b0c14269ab444250d1c0ecf81cd2562724de8f53a30c60ec"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\nREGISTERED INPUTS\n- damage_scene_1 [damage_scene] - First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"damage_scene_1\",\"damage_scene_2\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_1\",\"role\":\"scene_a\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_1\", \"question\": \"question_1\", \"role\": \"scene_a\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_scene_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_scene_2\",\"role\":\"scene_b\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_scene_2\", \"question\": \"question_1\", \"role\": \"scene_b\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"scene_a\\\": \\\"scene_1\\\", \\\"scene_b\\\": \\\"scene_2\\\"} into a `scene_compare` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"scene_a\":\"scene_1\",\"scene_b\":\"scene_2\"},\"mode\":\"scene_compare\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"scene_a\": \"scene_1\", \"scene_b\": \"scene_2\"}, \"mode\": \"scene_compare\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `pair_compare` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"pair_compare\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"woolsey-fire_00000419\", \"scenes\": [{\"classified_buildings\": 50, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 49, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 2.0, \"disaster\": \"woolsey-fire\", \"scene_id\": \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=db97260e3a4e]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"pair_compare\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"woolsey-fire_00000419\", \"scenes\": [{\"classified_buildings\": 50, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 49, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 2.0, \"disaster\": \"woolsey-fire\", \"scene_id\": \"woolsey-fire_00000568\", \"severe_count\": 1, \"severe_footprint_m2\": 6786.3652, \"severe_pct\": 2.0, \"total_buildings\": 51}, {\"classified_buildings\": 10, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 9, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 10.0, \"disaster\": \"woolsey-fire\", \"scene_id\": \"woolsey-fire_00000419\", \"severe_count\": 1, \"severe_footprint_m2\": 768.5366, \"severe_pct\": 10.0, \"total_bui...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Compare prepared scene summaries by `severe_damage_ratio_and_footprint`.\",\"action\":{\"tool\":\"CompareScenes\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_ratio_and_footprint\"}}}"}, {"role": "user", "content": "QUESTION\nCompare these two xBD scenes for the same event. Compute severe-damage percentage as 100 times the major-damage-or-destroyed building count divided by the classified-building count; exclude unclassified buildings from that denominator. Also compare severe-damage footprint area. Report both scene summaries and the higher scene for each metric; report an exact tie rather than selecting a winner.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q4 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"damage_scene_1\", \"damage_scene_2\"], \"objective\": \"xBD building-damage / XBD-Q4: prepare reusable evidence from damage_scene_1, damage_scene_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q4: interpret both prepared damage scenes and compare severe-damage burden. Report the worse scene with count/area/ratio support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_scene_1 [damage_scene] - damage_scene_1: First xBD scene for pairwise structural-damage comparison.\n- damage_scene_2 [damage_scene] - damage_scene_2: Second xBD scene for pairwise structural-damage comparison.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"compared scenes `scene_compare_1`\"\n- latest_handle = \"scene_compare_1\"\n- latest_tool = \"CompareScenes\"\n- latest_tool_ok = true\n- scene_compare_1_evidence = {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"woolsey-fire_00000419\", \"scenes\": [{\"classified_buildings\": 50, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 49, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 2.0, \"disaster\": \"woolsey-fire\", \"...\n- scene_compare_1_summary = \"compared scenes `scene_compare_1`\"\n- damage_1_evidence = {\"aggregation\": \"pair_compare\", \"greater_damage_scene\": \"woolsey-fire_00000419\", \"scenes\": [{\"classified_buildings\": 50, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 49, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 2.0, \"disaster\": \"woolsey-fire\", \"scene_id\": \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=db97260e3a4e]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_ratio_and_footprint\", \"question\": \"question_1\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"latest_handle\": \"scene_compare_1\", \"latest_summary\": \"compared scenes `scene_compare_1`\", \"latest_tool\": \"CompareScenes\", \"latest_tool_ok\": true, \"scene_compare_1_answer_ref\": \"ANSWER_JSON:scene_compare_1\", \"scene_compare_1_evidence\": {\"by\": \"severe_damage_ratio_and_footprint\", \"greater_damage_scene\": \"woolsey-fire_00000419\", \"scenes\": [{\"classified_buildings\": 50, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 49, \"unclassified\": 1}, \"damaged_count\": 1, \"damaged_pct\": 2.0, \"disaster\": \"woolsey-fire\", \"scene_id\": \"woolsey-fire_00000568\", \"severe_count\": 1, \"severe_footprint_m2\": 6786.3652, \"severe_pct\": 2.0, \"total_buildings\": 51}, {\"classified_buildings\": 10, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 0, \"minor-damage\": 0,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1376, "id": "xbd_damage/woolsey-fire_00000568/XBD-Q4", "dataset_id": "xbd_damage/woolsey-fire_00000568/XBD-Q4", "sample_id": "woolsey-fire_00000568", "question_type": "XBD-Q4", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "CompareScenes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "db97260e3a4ea4a0b937d08c9dcb5a1b10b8b99741319af2bf0df4747ffee948"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 6.31, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000173\", \"severe_count\": 0, \"severe_footpr...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4b9591a2f903]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 6.31, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000173\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 111, \"total_footprint_m2\": 1645881.4482}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 6.31, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harv...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 6.31, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000173\", \"severe_count\": 0, \"severe_footpr...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4b9591a2f903]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 111, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_count\": 7, \"damaged_pct\": 6.31, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000173\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 111, \"total_footpri...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1410, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q1", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4b9591a2f903f23c46ad6bf6869065a3877583605258842d9fdc065b96fecc52"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.101449, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"unclassified\": 0}, \"total_area_m2\": 150432.1751, \"unclassified\": 0}, {\"building_count\": 14, \"cell_id\": \"Q1\", \"classified_building_count\": 14, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"seve...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 23, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.101449, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 7, \"no_damage\": 16, \"unclassified\": 0}, \"total_area_m2\": 150432.1751, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidate...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1411, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q2", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G11\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G11\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 17783.4298, \"unclassified\": 0}, {\"building_count\": 6, \"cell_id\": \"G01\", \"classified_building_count\": 6, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 2, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.222222...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"G11\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G11\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7464acf0dae8]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"G11\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 17783.4298, \"unclassified\": 0}, {\"building_count\": 6, \"cell_id\": \"G01\", \"class...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1412, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q3", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7464acf0dae8dc392a88788377cafd11e941e603f845b8fb6de2f3bb03738eb5"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=075bc5dd0a0e]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1645881.4482}, \"damage_summary\": {\"classified_buildings\": 111, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 7, \"no-damage\": 104, \"unclassified\": 0}, \"damaged_pct\": 6.31, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 111}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"ma...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=075bc5dd0a0e]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1645881.4482}, \"damage_summary\": {\"classi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1413, "id": "xbd_damage/hurricane-harvey_00000173/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000173/XBD-Q8", "sample_id": "hurricane-harvey_00000173", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "075bc5dd0a0e605c325248dd8e075b9585c6f7fe4196978cc46644eeac6484a3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_count\": 35, \"damaged_pct\": 23.03, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000518\", \"severe_count\": 5, \"severe_f...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9b63939b047b]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_count\": 35, \"damaged_pct\": 23.03, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000518\", \"severe_count\": 5, \"severe_footprint_m2\": 11291.8279, \"severe_pct\": 3.29, \"total_buildings\": 153, \"total_footprint_m2\": 536707.7171}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_count\": 35, \"damaged_pct\": 23.03, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"scene_id\": \"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-michael) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1},...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_count\": 35, \"damaged_pct\": 23.03, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000518\", \"severe_count\": 5, \"severe_f...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9b63939b047b]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 152, \"damage_distribution\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_count\": 35, \"damaged_pct\": 23.03, \"disaster\": \"hurricane-michael\", \"scene_id\": \"hurricane-michael_00000518\", \"severe_count\": 5, \"severe_footprint_m2\": 11291.8279, \"severe_pct\": 3.29, \"total_buildings\": 153,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1458, "id": "xbd_damage/hurricane-michael_00000518/XBD-Q1", "dataset_id": "xbd_damage/hurricane-michael_00000518/XBD-Q1", "sample_id": "hurricane-michael_00000518", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9b63939b047bca08e0724c4052a1aa72dcdaa9f94c26e5b25c93edc888cedfac"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 36, \"cell_id\": \"Q1\", \"classified_building_count\": 35, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 8447.084, \"severe_count\": 3, \"se...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 36, \"cell_id\": \"Q1\", \"classified_building_count\": 35, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 8447.084, \"severe_count\": 3, \"severe_pct\": 8.5714, \"severity_score\": 0.152381, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"unclassified\": 1}, \"total_area_m2\": 105066.3996, \"unclassified\": 1}, {\"building_count\": 43, \"cell_id\": \"Q0\", \"classified_building_count\": 43, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 16, \"no_damage\": 26, \"row\": 0, \"severe_area_m2\": 2510.1371, \"severe_count\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-michael), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 36, \"cell_id\": \"Q1\", \"classified_building_count\": 35, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 8447.084,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 36, \"cell_id\": \"Q1\", \"classified_building_count\": 35, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 8447.084, \"severe_count\": 3, \"se...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=227847f215a4]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 36, \"cell_id\": \"Q1\", \"classified_building_count\": 35, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"row\": 0, \"severe_area_m2\": 8447.084, \"severe_count\": 3, \"severe_pct\": 8.5714, \"severity_score\": 0.152381, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 3, \"minor_damage\": 10, \"no_damage\": 22, \"unclassified\": 1}, \"total_area_m2\": 105066.3996, \"unclassified\": 1}]}, \"rank_1_payload\": {\"all...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1459, "id": "xbd_damage/hurricane-michael_00000518/XBD-Q2", "dataset_id": "xbd_damage/hurricane-michael_00000518/XBD-Q2", "sample_id": "hurricane-michael_00000518", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "227847f215a44f9abd4635b7018c175d737756f3d13d7bca163740eaafc0f674"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 2919.728, \"severe_count\": 1, \"sever...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 2919.728, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 4384.199, \"unclassified\": 0}, {\"building_count\": 10, \"cell_id\": \"G12\", \"classified_building_count\": 10, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 1, \"minor_damage\": 5, \"no_damage\": 4, \"row\": 1, \"severe_area_m2\": 5381.5924, \"severe_count\": 1, \"severe_pct\": 10.0, \"severity...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 2919.728, \"s...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 2919.728, \"severe_count\": 1, \"sever...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=784f61404cc4]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 2919.728, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 4384.199, \"unclassified\": 0}, {\"building_count\": 10, \"cell_id\": \"G12...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1460, "id": "xbd_damage/hurricane-michael_00000518/XBD-Q3", "dataset_id": "xbd_damage/hurricane-michael_00000518/XBD-Q3", "sample_id": "hurricane-michael_00000518", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "784f61404cc486a3f4258da63ee53ceadc8bae036ed8cb7e49c864994f0277f3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7ea796b3233e]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 11291.8279, \"total_footprint_m2\": 536707.7171}, \"damage_summary\": {\"classified_buildings\": 152, \"counts\": {\"destroyed\": 1, \"major-damage\": 4, \"minor-damage\": 30, \"no-damage\": 117, \"unclassified\": 1}, \"damaged_pct\": 23.03, \"severe_count\": 5, \"severe_pct\": 3.29, \"total_buildings\": 153}, \"disaster\": \"hurricane-michael\", \"disaster_type\": \"wind\", \"gsd\": 2.071191, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destro...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-michael) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7ea796b3233e]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-michael\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 11291.8279, \"total_footprint_m2\": 536707.7171}, \"damage_summary\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1463, "id": "xbd_damage/hurricane-michael_00000518/XBD-Q8", "dataset_id": "xbd_damage/hurricane-michael_00000518/XBD-Q8", "sample_id": "hurricane-michael_00000518", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7ea796b3233e67d53dccfc7e73468b02e3ec0486336f0558d9f4700804c60005"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 1, \"sample_objects\": [{\"area_m2\": 421116.2196, \"centroid_x\": 250.4419, \"centroid_y\": 456.548, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 421116.22}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 1, \"sample_objects\": [{\"area_m2\": 421116.2196, \"centroid_x\": 250.4419, \"centroid_y\": 456.548, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 421116.22}, \"objects_1_payload\": {\"object_count\": 1, \"sample_objects\": [{\"area_m2\": 421116.2196, \"centroid_x\": 250.4419, \"centroid_y\": 456.548, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 421116.22}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_ar...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 1, \"severe_footprint_m2\": 421116.2196}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 1, \"sample_objects\": [{\"area_m2\": 421116.2196, \"centroid_x\": 250.4419, \"centroid_y\": 456.548, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 421116.22}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=35f19157da00]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 1, \"severe_footprint_m2\": 421116.2196}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 421116.2196, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [{\"area_m2\": 421116.2196, \"centroid_x\": 250.4419, \"centroid_y\": 456.548, \"damage_score\": 2, \"subtype\": \"major-damage\"}], \"severe_building_count\": 1, \"severe_count\": 1, \"severe_footprint_m2\": 421116.2196}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1479, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q5", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q5", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "35f19157da009b9888f772f809bd29ceadd64da53c352b116217c6615401bf74"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 259154.8967, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"Q2\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 2, \"row\": 1, \"severe_area_m2\": 162872.7231, \"sever...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"s...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"severe_pct\": 50.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 259154.8967, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"Q1\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"s...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 2, \"cell_id\": \"Q0\", \"classified_building_count\": 2, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 1, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 258243.4965, \"severe_count\": 1, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=557dd48aa11b]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"concentration_status\": \"scored\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 1, \"per_quadrant_severe\": {\"Q0\": 1, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [\"Q0\"], \"total_severe\": 1, \"worst_quadrant\": \"Q0\"}, \"score_1_payload_...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1480, "id": "xbd_damage/hurricane-michael_00000548/XBD-Q7", "dataset_id": "xbd_damage/hurricane-michael_00000548/XBD-Q7", "sample_id": "hurricane-michael_00000548", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "557dd48aa11b2d27c87903c88782f9c359631c79e78371a11909b8698ff47678"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 28.57, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00001267\", \"severe_count\": 4, \"severe_footprint_m2\": 3210...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=cc65cea23add]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 28.57, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00001267\", \"severe_count\": 4, \"severe_footprint_m2\": 32107.0949, \"severe_pct\": 28.57, \"total_buildings\": 14, \"total_footprint_m2\": 80769.4757}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 28.57, \"disaster\": \"socal-fire\", \"disaster_type\": \"fire\", \"gsd\": 2.570075, \"scene_id\": \"socal-fire_00001267\", \"severe_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 28.57, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00001267\", \"severe_count\": 4, \"severe_footprint_m2\": 3210...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=cc65cea23add]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 14, \"damage_distribution\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 28.57, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00001267\", \"severe_count\": 4, \"severe_footprint_m2\": 32107.0949, \"severe_pct\": 28.57, \"total_buildings\": 14, \"total_footprint_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1512, "id": "xbd_damage/socal-fire_00001267/XBD-Q1", "dataset_id": "xbd_damage/socal-fire_00001267/XBD-Q1", "sample_id": "socal-fire_00001267", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "cc65cea23addaf83222636b9e30e23bddba4be907c0839b1a98df02fb73785f2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"Q2\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"row\": 1, \"severe_area_m2\": 22458.1292, \"severe_count\": 1, \"seve...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"Q2\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"row\": 1, \"severe_area_m2\": 22458.1292, \"severe_count\": 1, \"severe_pct\": 25.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"unclassified\": 0}, \"total_area_m2\": 31445.657, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"Q1\", \"classified_building_count\": 8, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 6974.5661, \"severe_count\": 2, \"severe_pct...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"Q2\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"row\": 1, \"severe_area_m2\": 22458.1292, \"...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"Q2\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"row\": 1, \"severe_area_m2\": 22458.1292, \"severe_count\": 1, \"seve...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4881f17aaa4e]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"Q2\", \"classified_building_count\": 4, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"row\": 1, \"severe_area_m2\": 22458.1292, \"severe_count\": 1, \"severe_pct\": 25.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 3, \"unclassified\": 0}, \"total_area_m2\": 31445.657, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1513, "id": "xbd_damage/socal-fire_00001267/XBD-Q2", "dataset_id": "xbd_damage/socal-fire_00001267/XBD-Q2", "sample_id": "socal-fire_00001267", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4881f17aaa4ec5762e8317851d8d24c90674e5fd4f11a9daa95094a834dc865d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3317.2706, \"severe_count\": 1, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3317.2706, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 3373.3742, \"unclassified\": 0}, {\"building_count\": 1, \"cell_id\": \"G33\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 2674.3995, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3317.2706, \"sev...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3317.2706, \"severe_count\": 1, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ebbcd7466ad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3317.2706, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 3373.3742, \"unclassified\": 0}, {\"building_count\": 1, \"cell_id\": \"G33\", \"cl...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1514, "id": "xbd_damage/socal-fire_00001267/XBD-Q3", "dataset_id": "xbd_damage/socal-fire_00001267/XBD-Q3", "sample_id": "socal-fire_00001267", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "6ebbcd7466ade6dbe1e09c303ccd667d92a5d4c5e19717de5df30c561948478e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ef1159f240b3]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 32107.0949, \"total_footprint_m2\": 80769.4757}, \"damage_summary\": {\"classified_buildings\": 14, \"counts\": {\"destroyed\": 4, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 10, \"unclassified\": 0}, \"damaged_pct\": 28.57, \"severe_count\": 4, \"severe_pct\": 28.57, \"total_buildings\": 14}, \"disaster\": \"socal-fire\", \"disaster_type\": \"fire\", \"gsd\": 2.570075, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_availa...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ef1159f240b3]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 32107.0949, \"total_footprint_m2\": 80769.4757}, \"damage_summary\": {\"class...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1517, "id": "xbd_damage/socal-fire_00001267/XBD-Q8", "dataset_id": "xbd_damage/socal-fire_00001267/XBD-Q8", "sample_id": "socal-fire_00001267", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ef1159f240b322d10f0df6666554b9482f9b0eab7acc4af725924d47f9c0266f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_count\": 13, \"damaged_pct\": 61.9, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00000202\", \"severe_count\": 13, \"severe_footprint_m2\": 386...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1d170fecec1f]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_count\": 13, \"damaged_pct\": 61.9, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00000202\", \"severe_count\": 13, \"severe_footprint_m2\": 38627.1215, \"severe_pct\": 61.9, \"total_buildings\": 21, \"total_footprint_m2\": 62736.7148}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_count\": 13, \"damaged_pct\": 61.9, \"disaster\": \"socal-fire\", \"disaster_type\": \"fire\", \"gsd\": 2.570075, \"scene_id\": \"socal-fire_00000202\", \"severe_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a fire event (socal-fire) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_count\": 13, \"damaged_pct\": 61.9, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00000202\", \"severe_count\": 13, \"severe_footprint_m2\": 386...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1d170fecec1f]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 21, \"damage_distribution\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_count\": 13, \"damaged_pct\": 61.9, \"disaster\": \"socal-fire\", \"scene_id\": \"socal-fire_00000202\", \"severe_count\": 13, \"severe_footprint_m2\": 38627.1215, \"severe_pct\": 61.9, \"total_buildings\": 21, \"total_footprint_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1524, "id": "xbd_damage/socal-fire_00000202/XBD-Q1", "dataset_id": "xbd_damage/socal-fire_00000202/XBD-Q1", "sample_id": "socal-fire_00000202", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1d170fecec1feb016e09843063b862b6c19840eba1841c92750e21e9ce9e3309"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 18, \"cell_id\": \"Q3\", \"classified_building_count\": 18, \"col\": 1, \"destroyed\": 10, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 1, \"severe_area_m2\": 34354.6974, \"severe_count\": 10, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 18, \"cell_id\": \"Q3\", \"classified_building_count\": 18, \"col\": 1, \"destroyed\": 10, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 1, \"severe_area_m2\": 34354.6974, \"severe_count\": 10, \"severe_pct\": 55.5556, \"severity_score\": 0.555556, \"subtype_histogram\": {\"destroyed\": 10, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"unclassified\": 0}, \"total_area_m2\": 58464.2907, \"unclassified\": 0}, {\"building_count\": 1, \"cell_id\": \"Q2\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 3057.8557, \"severe_count\": 1...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster fire scene (socal-fire), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 18, \"cell_id\": \"Q3\", \"classified_building_count\": 18, \"col\": 1, \"destroyed\": 10, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 1, \"severe_area_m2\": 34354.6974...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 18, \"cell_id\": \"Q3\", \"classified_building_count\": 18, \"col\": 1, \"destroyed\": 10, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 1, \"severe_area_m2\": 34354.6974, \"severe_count\": 10, \"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4e8a250f4da3]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 18, \"cell_id\": \"Q3\", \"classified_building_count\": 18, \"col\": 1, \"destroyed\": 10, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"row\": 1, \"severe_area_m2\": 34354.6974, \"severe_count\": 10, \"severe_pct\": 55.5556, \"severity_score\": 0.555556, \"subtype_histogram\": {\"destroyed\": 10, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 8, \"unclassified\": 0}, \"total_area_m2\": 58464.2907, \"unclassified\": 0}]}, \"rank_1_payload\": {\"al...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1525, "id": "xbd_damage/socal-fire_00000202/XBD-Q2", "dataset_id": "xbd_damage/socal-fire_00000202/XBD-Q2", "sample_id": "socal-fire_00000202", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4e8a250f4da3d97c6f5e9bc6ea491f351cae39825aa9c8b96dc7c509db610131"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G12\", \"classified_building_count\": 1, \"col\": 2, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 682.3311, \"severe_count\": 1, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G12\", \"classified_building_count\": 1, \"col\": 2, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 682.3311, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 682.3311, \"unclassified\": 0}, {\"building_count\": 1, \"cell_id\": \"G13\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 532.2372, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G12\", \"classified_building_count\": 1, \"col\": 2, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 682.3311, \"seve...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G12\", \"classified_building_count\": 1, \"col\": 2, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 682.3311, \"severe_count\": 1, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1f703743881b]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G12\", \"classified_building_count\": 1, \"col\": 2, \"destroyed\": 1, \"dominant_damage\": \"destroyed\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 682.3311, \"severe_count\": 1, \"severe_pct\": 100.0, \"severity_score\": 1.0, \"subtype_histogram\": {\"destroyed\": 1, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 682.3311, \"unclassified\": 0}, {\"building_count\": 1, \"cell_id\": \"G13\", \"clas...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1526, "id": "xbd_damage/socal-fire_00000202/XBD-Q3", "dataset_id": "xbd_damage/socal-fire_00000202/XBD-Q3", "sample_id": "socal-fire_00000202", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1f703743881b0da694a50ad1cdb067b2ab4fb5b1433003abbdfa1e81add5b279"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a44f1b3c997a]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 38627.1215, \"total_footprint_m2\": 62736.7148}, \"damage_summary\": {\"classified_buildings\": 21, \"counts\": {\"destroyed\": 13, \"major-damage\": 0, \"minor-damage\": 0, \"no-damage\": 8, \"unclassified\": 0}, \"damaged_pct\": 61.9, \"severe_count\": 13, \"severe_pct\": 61.9, \"total_buildings\": 21}, \"disaster\": \"socal-fire\", \"disaster_type\": \"fire\", \"gsd\": 2.570075, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_availab...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster fire scene (socal-fire) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a44f1b3c997a]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"socal-fire\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 38627.1215, \"total_footprint_m2\": 62736.7148}, \"damage_summary\": {\"class...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1529, "id": "xbd_damage/socal-fire_00000202/XBD-Q8", "dataset_id": "xbd_damage/socal-fire_00000202/XBD-Q8", "sample_id": "socal-fire_00000202", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a44f1b3c997a9898fed5a9202910afca050f564035373162a40113c191014b10"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000188\", \"severe_count\": 0, \"severe_footprin...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7412db3122c3]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000188\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 37, \"total_footprint_m2\": 1633099.409}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harvey_000...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"da...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000188\", \"severe_count\": 0, \"severe_footprin...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=7412db3122c3]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 37, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_count\": 3, \"damaged_pct\": 8.11, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000188\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 37, \"total_footprint_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1597, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q1", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "7412db3122c3813887dbbc044dd351f1113c97c72c5cfc54d19bfddce7084ea8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"unclassified\": 0}, \"total_area_m2\": 965421.0393, \"unclassified\": 0}, {\"building_count\": 8, \"cell_id\": \"Q1\", \"classified_building_count\": 8, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"seve...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 14, \"cell_id\": \"Q0\", \"classified_building_count\": 14, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 14, \"unclassified\": 0}, \"total_area_m2\": 965421.0393, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\": [...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1598, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q2", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G03\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G03\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 59214.0496, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.33...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G03\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"G03\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5b81377eecc]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"G03\", \"classified_building_count\": 1, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 59214.0496, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G13\", \"c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1599, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q3", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5b81377eeccb53dc06cb69a1d75a2ab4860a8e7c0c7f1fab5ac61662cba49f4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c5fefd222b17]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1633099.409}, \"damage_summary\": {\"classified_buildings\": 37, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 3, \"no-damage\": 34, \"unclassified\": 0}, \"damaged_pct\": 8.11, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 37}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_a...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c5fefd222b17]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 1633099.409}, \"damage_summary\": {\"classif...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1600, "id": "xbd_damage/hurricane-harvey_00000188/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000188/XBD-Q8", "sample_id": "hurricane-harvey_00000188", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c5fefd222b170af78d965a055297da86d1c9b49f680e7410a54ad633eaaa70cc"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_count\": 16, \"damaged_pct\": 51.61, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000087\", \"severe_count\": 0, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0f16dc072ef0]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_count\": 16, \"damaged_pct\": 51.61, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000087\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 31, \"total_footprint_m2\": 551076.9052}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_count\": 16, \"damaged_pct\": 51.61, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harv...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"d...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_count\": 16, \"damaged_pct\": 51.61, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000087\", \"severe_count\": 0, \"severe_footp...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0f16dc072ef0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 31, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_count\": 16, \"damaged_pct\": 51.61, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000087\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 31, \"total_footpri...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1609, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q1", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0f16dc072ef049fee91cd041880484b6c17f215a4c0ec5ee350ea81d2fbd9388"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.083333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 150454.2012, \"unclassified\": 0}, {\"building_count\": 6, \"cell_id\": \"Q1\", \"classified_building_count\": 6, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 6, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.083333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 150454.2012, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1610, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q2", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G12\", \"classified_building_count\": 4, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G12\", \"classified_building_count\": 4, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 86738.1315, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G13\", \"classified_building_count\": 2, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 2, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.33...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"G12\", \"classified_building_count\": 4, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 4, \"cell_id\": \"G12\", \"classified_building_count\": 4, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3ab652fd0872]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 4, \"cell_id\": \"G12\", \"classified_building_count\": 4, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.333333, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 4, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 86738.1315, \"unclassified\": 0}, {\"building_count\": 2, \"cell_id\": \"G13\", \"c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1611, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q3", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3ab652fd08727f49cd956bf007ede4e5a0c0411ed794225a081d6dba82261726"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=59254fba63b8]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 551076.9052}, \"damage_summary\": {\"classified_buildings\": 31, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 16, \"no-damage\": 15, \"unclassified\": 0}, \"damaged_pct\": 51.61, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 31}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=59254fba63b8]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 551076.9052}, \"damage_summary\": {\"classif...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1612, "id": "xbd_damage/hurricane-harvey_00000087/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000087/XBD-Q8", "sample_id": "hurricane-harvey_00000087", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "59254fba63b8120cc7b633625fbb734c7c8b35051a31a3efce31d6acdbb0c593"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 30.77, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000083\", \"severe_count\": 0, \"severe_footprin...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=124b4b70b2aa]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 30.77, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000083\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 13, \"total_footprint_m2\": 66451.0977}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 30.77, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"scene_id\": \"hurricane-harvey_0000...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"dam...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 30.77, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000083\", \"severe_count\": 0, \"severe_footprin...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=124b4b70b2aa]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 13, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_count\": 4, \"damaged_pct\": 30.77, \"disaster\": \"hurricane-harvey\", \"scene_id\": \"hurricane-harvey_00000083\", \"severe_count\": 0, \"severe_footprint_m2\": 0, \"severe_pct\": 0.0, \"total_buildings\": 13, \"total_footprint_...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1613, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q1", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q1", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "124b4b70b2aa86c802929aadf053cf93097de585866c252e900f961e97ac16b4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 5971.6719, \"unclassified\": 0}, {\"building_count\": 4, \"cell_id\": \"Q1\", \"classified_building_count\": 4, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 3, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severi...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster flooding scene (hurricane-harvey), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\"...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=43fe36bffdad]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 1, \"cell_id\": \"Q0\", \"classified_building_count\": 1, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 1, \"unclassified\": 0}, \"total_area_m2\": 5971.6719, \"unclassified\": 0}]}, \"rank_1_payload\": {\"all_candidates\": [{\"buil...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1614, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q2", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q2", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "43fe36bffdade7ba64b44da1a2c40974a67c79bcebe51275182cbd1bf32753a9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"G23\", \"classified_building_count\": 8, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 2, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"G23\", \"classified_building_count\": 8, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 2, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.125, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"unclassified\": 0}, \"total_area_m2\": 8925.6498, \"unclassified\": 0}, {\"building_count\": 3, \"cell_id\": \"G02\", \"classified_building_count\": 3, \"col\": 2, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 1, \"no_damage\": 2, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.111111, \"sub...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"G23\", \"classified_building_count\": 8, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 2, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 8, \"cell_id\": \"G23\", \"classified_building_count\": 8, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 2, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=154e497c1237]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 8, \"cell_id\": \"G23\", \"classified_building_count\": 8, \"col\": 3, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"row\": 2, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.125, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 3, \"no_damage\": 5, \"unclassified\": 0}, \"total_area_m2\": 8925.6498, \"unclassified\": 0}, {\"building_count\": 3, \"cell_id\": \"G02\", \"classifi...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1615, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q3", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q3", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "154e497c1237a3b5c9cdc9d54d6b3fdd4fb963c99114c10d7c982006640136c9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9b7562b92c80]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 66451.0977}, \"damage_summary\": {\"classified_buildings\": 13, \"counts\": {\"destroyed\": 0, \"major-damage\": 0, \"minor-damage\": 4, \"no-damage\": 9, \"unclassified\": 0}, \"damaged_pct\": 30.77, \"severe_count\": 0, \"severe_pct\": 0.0, \"total_buildings\": 13}, \"disaster\": \"hurricane-harvey\", \"disaster_type\": \"flooding\", \"gsd\": 3.024613, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroyed\", \"mask_av...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster flooding scene (hurricane-harvey) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9b7562b92c80]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-harvey\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 0, \"total_footprint_m2\": 66451.0977}, \"damage_summary\": {\"classifi...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1616, "id": "xbd_damage/hurricane-harvey_00000083/XBD-Q8", "dataset_id": "xbd_damage/hurricane-harvey_00000083/XBD-Q8", "sample_id": "hurricane-harvey_00000083", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9b7562b92c806e3a242a0032a1d0b59a9039c592bd31c36e80f391c8af15e015"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 61, \"sample_objects\": [{\"area_m2\": 8182.9441, \"centroid_x\": 9.2872, \"centroid_y\": 916.8918, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 32648.8331, \"centroid_x\": 69.3302, \"centroid_y\": 873.5383, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"tar...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 61, \"sample_objects\": [{\"area_m2\": 8182.9441, \"centroid_x\": 9.2872, \"centroid_y\": 916.8918, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 32648.8331, \"centroid_x\": 69.3302, \"centroid_y\": 873.5383, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 755352.054}, \"objects_1_payload\": {\"object_count\": 61, \"sample_objects\": [{\"area_m2\": 8182.9441, \"centroid_x\": 9.2872, \"centroid_y\": 916.8918, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"majo...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 61, \"severe_footprint_m2\": 755352.0537}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 61, \"sample_objects\": [{\"area_m2\": 8182.9441, \"centroid_x\": 9.2872, \"centroid_y\": 916.8918, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 32648.8331, \"centroid_x\": 69.3302, \"centroid_y\": 873.5383, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"tar...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d40d75a2a234]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 61, \"severe_footprint_m2\": 755352.0537}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 755352.0537, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [{\"area_m2\": 8182.9441, \"centroid_x\": 9.2872, \"centroid_y\": 916.8918, \"damage_score\": 2, \"subtype\": \"major-damage\"}, {\"area_m2\": 32648.8331, \"centroid_x\": 69.3302, \"centroid_y\": 873.5383, \"damage_score\": 2, \"subtype\": \"major-damage\"}, {\"area_m2\": 28059.2974, \"centroid_x\": 99.0046, \"centroid_y\": 806.093, \"damage_score\": 2, \"subtype\": \"major-damage\"}, {\"area_m2\": 7258.0243, \"c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1644, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q5", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d40d75a2a234a19994bf21df51f7c586b16bbc5611062617b3ae7bf65e24d987"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\": 34, \"severe_pct\": 73.913, \"severity_score\": 0.57971, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 539080.6625, \"unclassified\": 0}, {\"building_count\": 57, \"cell_id\": \"Q1\", \"classified_building_count\": 54, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 22, \"minor_damage\": 32, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 2821...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\": 34, \"severe_pct\": 73.913, \"severity_score\": 0.57971, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 539080.6625, \"unclassified\": 0}, {\"building_count\": 57, \"cell_...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"moderately concentrated\", \"concentration_score\": 0.5574, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 46, \"cell_id\": \"Q0\", \"classified_building_count\": 46, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"major-damage\", \"major_damage\": 34, \"minor_damage\": 12, \"no_damage\": 0, \"row\": 0, \"severe_area_m2\": 381141.6547, \"severe_count\":...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fbf5be2d3753]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"moderately concentrated\", \"concentration_score\": 0.5574, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"moderately concentrated\", \"concentration_score\": 0.5574, \"concentration_status\": \"scored\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 34, \"per_quadrant_severe\": {\"Q0\": 34, \"Q1\": 22, \"Q2\": 5, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [\"Q0\"], \"total_severe\": 61, \"worst_quadrant\": \"Q0\"},...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1645, "id": "xbd_damage/hurricane-harvey_00000351/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000351/XBD-Q7", "sample_id": "hurricane-harvey_00000351", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "fbf5be2d3753c6e1dbecba39a17bb1cd015a8632e82b82ba991a6f56e9ad1da4"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_count\": 106, \"damaged_pct\": 89.83, \"disaster\": \"hurricane-matthew\", \"scene_id\": \"hurricane-matthew_00000073\", \"severe_count\": 2, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=731d24935dc7]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_count\": 106, \"damaged_pct\": 89.83, \"disaster\": \"hurricane-matthew\", \"scene_id\": \"hurricane-matthew_00000073\", \"severe_count\": 2, \"severe_footprint_m2\": 9483.1404, \"severe_pct\": 1.69, \"total_buildings\": 126, \"total_footprint_m2\": 363813.9057}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_count\": 106, \"damaged_pct\": 89.83, \"disaster\": \"hurricane-matthew\", \"disaster_type\": \"wind\", \"gsd\": 2.378877, \"scene_id\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"building_count_by_damage\\\": true, \\\"severe_ratio\\\": true, \\\"footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"building_count_by_damage\":true,\"severe_ratio\":true,\"footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nA post-disaster satellite scene from a wind event (hurricane-matthew) has been captured. From the declared building-damage polygons, report counts by class, damaged and severe percentages over classified buildings only, and total severe-building footprint area in square metres.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q1 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q1: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q1: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q1: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8},...\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_count\": 106, \"damaged_pct\": 89.83, \"disaster\": \"hurricane-matthew\", \"scene_id\": \"hurricane-matthew_00000073\", \"severe_count\": 2, \"severe_...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=731d24935dc7]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"building_count_by_damage\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"building_count_by_damage\": true, \"damage_summary\": true, \"footprint_area\": true, \"severe_ratio\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"classified_buildings\": 118, \"damage_distribution\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_count\": 106, \"damaged_pct\": 89.83, \"disaster\": \"hurricane-matthew\", \"scene_id\": \"hurricane-matthew_00000073\", \"severe_count\": 2, \"severe_footprint_m2\": 9483.1404, \"severe_pct\": 1.69, \"total_buildings\": 126,...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1671, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q1", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q1", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q1", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "731d24935dc79d6542a92c088aab0befcd24709d72b3690f7d338900240a4a09"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `severe`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"severe\"}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"severe\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2, \"severe_pct\": 7.4074, \"severity_score\": 0.345679, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"unclassified\": 1}, \"total_area_m2\": 94171.0166, \"unclassified\": 1}, {\"building_count\": 30, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 19, \"no_damage\": 4, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_footprint_area_m2` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_footprint_area_m2\",\"candidates\":\"quadrants_2x2\",\"top_k\":1}}}"}, {"role": "user", "content": "QUESTION\nFor this post-disaster wind scene (hurricane-matthew), divide the 1024×1024 frame into Q0–Q3. Rank quadrants by severe polygon area clipped to each quadrant; assign each severe-building count to its centroid quadrant. Report the ascending-ID representative of the maximum-area tie, its centroid count, and its clipped footprint area.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q2 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q2: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q2: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q2: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.14...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2b8c9f966942]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 1, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_footprint_area_m2\", \"candidates\": \"quadrants_2x2\", \"top_k\": 1, \"top_ranked\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2, \"severe_pct\": 7.4074, \"severity_score\": 0.345679, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"unclassified\": 1}, \"total_area_m2\": 94171.0166, \"unclassified\": 1}]}, \"rank_1_payload\": {\"al...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1672, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q2", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q2", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q2", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2b8c9f9669421192ad55e33778cc0bda28645842c8865138638fcbca4c7eb51a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_grid` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_grid\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_grid\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `grid_4x4` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"grid_4x4\"}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 5, \"cell_id\": \"G31\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 9458.6294, \"severe_count\": 2, \"seve...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"grid_4x4\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 5, \"cell_id\": \"G31\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 9458.6294, \"severe_count\": 2, \"severe_pct\": 40.0, \"severity_score\": 0.466667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 19993.3382, \"unclassified\": 0}, {\"building_count\": 5, \"cell_id\": \"G11\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 5, \"no_damage\": 0, \"row\": 1, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `normalized_damage_severity` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"normalized_damage_severity\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nOn the 4×4 grid, rank cells containing at least one classified building by the exact score (minor + 2×major + 3×destroyed)/(3×classified). Exclude unclassified buildings from the denominator and cells with no classified buildings from ranking. Report the ascending-ID representative and all exact top ties.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q3 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q3: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q3: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q3: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 5, \"cell_id\": \"G31\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 9458.6294, \"...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"grid_4x4\", \"cell_count\": 16, \"top_severe_cells\": [{\"building_count\": 5, \"cell_id\": \"G31\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 9458.6294, \"severe_count\": 2, \"seve...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3df11e31b579]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"normalized_damage_severity\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"building_count\": 5, \"cell_id\": \"G31\", \"classified_building_count\": 5, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"row\": 3, \"severe_area_m2\": 9458.6294, \"severe_count\": 2, \"severe_pct\": 40.0, \"severity_score\": 0.466667, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 3, \"no_damage\": 0, \"unclassified\": 0}, \"total_area_m2\": 19993.3382, \"unclassified\": 0}, {\"building_count\": 5, \"cell_id\": \"G...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1673, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q3", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q3", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q3", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3df11e31b5798af03aa296ee8e7737709a9f9e871e30e4d3888345203168191d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - Target masks and class-distribution layers.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\",\"mask_layers_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `mask_layers_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"mask_layers_1\",\"role\":\"target_classes\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_3`\"\n- latest_handle = \"scene_3\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"mask_layers_1\", \"question\": \"question_1\", \"role\": \"target_classes\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_3\", \"latest_summary\": \"opened input as `scene_3`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_3_summary\": \"opened input as `scene_3`\"}, \"layers\": []}, \"text\": \"opened input as `scene_3`\", \"tool\": \"ReadData\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\", \\\"masks\\\": \\\"scene_3\\\"} into a `xbd_scene_summary` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\",\"masks\":\"scene_3\"},\"mode\":\"xbd_scene_summary\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\", \"masks\": \"scene_3\"}, \"mode\": \"xbd_scene_summary\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `scene` with severity `all`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-matthew\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=cf7cbb7b2dc1]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"scene\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-matthew\"}, \"damage_1_payload\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 9483.1404, \"total_footprint_m2\": 363813.9057}, \"damage_summary\": {\"classified_buildings\": 118, \"counts\": {\"destroyed\": 0, \"major-damage\": 2, \"minor-damage\": 104, \"no-damage\": 12, \"unclassified\": 8}, \"damaged_pct\": 89.83, \"severe_count\": 2, \"severe_pct\": 1.69, \"total_buildings\": 126}, \"disaster\": \"hurricane-matthew\", \"disaster_type\": \"wind\", \"gsd\": 2.378877, \"mask_validation\": {\"class_presence_checks\": {\"destroyed\": true, \"major-damage\": true, \"minor-damage\": true, \"no-damage\": true}, \"class_presence_consistent\": true, \"encoding\": \"0=background,1=no-damage,2=minor-damage,3=major-damage,4=destroy...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The prepared work context contains the needed evidence; compute metrics {\\\"damage_summary\\\": true, \\\"target_mask_distribution\\\": true, \\\"severe_footprint_area\\\": true}.\",\"action\":{\"tool\":\"ComputeStats\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"metrics\":{\"damage_summary\":true,\"target_mask_distribution\":true,\"severe_footprint_area\":true},\"region\":\"scene\"}}}"}, {"role": "user", "content": "QUESTION\nCreate a comprehensive summary for this post-disaster wind scene (hurricane-matthew) from the declared building polygons, 2×2 quadrant table, and target mask. Include classified-only damage percentages, severe footprint area, spatial concentration (null winner and score when no severe buildings), and validation of mask codes 0–4; unclassified polygons are intentionally absent from the mask.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q8 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\", \"mask_layers_1\"], \"objective\": \"xBD building-damage / XBD-Q8: prepare reusable evidence from image_1, damage_labels_1, mask_layers_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q8: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q8: compute the requested statistic from prepared evidence. Report the value, units, and support fields needed by ORC.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- mask_layers_1 [mask_layers] - mask_layers_1: Target masks and class-distribution layers.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"computed statistics `stats_1`\"\n- latest_handle = \"stats_1\"\n- latest_tool = \"ComputeStats\"\n- latest_tool_ok = true\n- stats_1_evidence = {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-matthew\"}}\n- stats_1_summary = \"computed statistics `stats_1`\"\n- damage_1_evidence = {\"aggregation\": \"scene\", \"disaster\": \"hurricane-matthew\"}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_3_summary = \"opened input as `scene_3`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=cf7cbb7b2dc1]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"question\": \"question_1\", \"region\": \"scene\", \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:stats_1\", \"latest_handle\": \"stats_1\", \"latest_summary\": \"computed statistics `stats_1`\", \"latest_tool\": \"ComputeStats\", \"latest_tool_ok\": true, \"stats_1_answer_ref\": \"ANSWER_JSON:stats_1\", \"stats_1_evidence\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"disaster\": \"hurricane-matthew\"}}, \"stats_1_payload\": {\"metrics\": {\"damage_summary\": true, \"severe_footprint_area\": true, \"target_mask_distribution\": true}, \"region\": \"scene\", \"stats\": {\"aggregation\": \"scene\", \"area_summary\": {\"area_method\": \"xy_polygon_area_times_gsd_squared\", \"severe_footprint_m2\": 9483.1404, \"total_footprint_m2\": 363813.9057}, \"damage_summary\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1676, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q8", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q8", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q8", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "ReadData", "PrepareScene", "AssessDamage", "ComputeStats"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "cf7cbb7b2dc1fac0b951ce4882c7411fc50fea33b8cd82ac51015834effbd192"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 4, \"sample_objects\": [{\"area_m2\": 939.4572, \"centroid_x\": 371.1603, \"centroid_y\": 382.7076, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 914.1175, \"centroid_x\": 732.0873, \"centroid_y\": 227.0408, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 4, \"sample_objects\": [{\"area_m2\": 939.4572, \"centroid_x\": 371.1603, \"centroid_y\": 382.7076, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 914.1175, \"centroid_x\": 732.0873, \"centroid_y\": 227.0408, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 12763.616}, \"objects_1_payload\": {\"object_count\": 4, \"sample_objects\": [{\"area_m2\": 939.4572, \"centroid_x\": 371.1603, \"centroid_y\": 382.7076, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"},...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 4, \"severe_footprint_m2\": 12763.6158}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 4, \"sample_objects\": [{\"area_m2\": 939.4572, \"centroid_x\": 371.1603, \"centroid_y\": 382.7076, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 914.1175, \"centroid_x\": 732.0873, \"centroid_y\": 227.0408, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6bf99c2f0ce3]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 4, \"severe_footprint_m2\": 12763.6158}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 2698.0822, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 10065.5336, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [{\"area_m2\": 939.4572, \"centroid_x\": 371.1603, \"centroid_y\": 382.7076, \"damage_score\": 3, \"subtype\": \"destroyed\"}, {\"area_m2\": 914.1175, \"centroid_x\": 732.0873, \"centroid_y\": 227.0408, \"damage_score\": 3, \"subtype\": \"destroyed\"}, {\"area_m2\": 844.5075, \"centroid_x\": 731.9781, \"centroid_y\": 240.0538, \"damage_score\": 3, \"subtype\": \"destroyed\"}, {\"area_m2\": 10065.5336, \"cent...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1680, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q5", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q5", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "6bf99c2f0ce31e39aaa79f954141ee1c8f5f470fcd61497ba774e265c5cddeba"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"severe_pct\": 15.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"unclassified\": 0}, \"total_area_m2\": 70729.9731, \"unclassified\": 0}, {\"building_count\": 9, \"cell_id\": \"Q0\", \"classified_building_count\": 8, \"col\": 0, \"destroyed\": 1, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 7, \"row\": 0, \"severe_area_m2\": 939.4572, \"severe_count\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"se...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"severe_pct\": 15.0, \"severity_score\": 0.25, \"subtype_histogram\": {\"destroyed\": 2, \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"unclassified\": 0}, \"total_area_m2\": 70729.9731, \"unclassified\": 0}, {\"building_count\": 9, \"cell_id\": \"Q0\", \"cl...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"highly concentrated\", \"concentration_score\": 0.75, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"se...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 20, \"cell_id\": \"Q1\", \"classified_building_count\": 20, \"col\": 1, \"destroyed\": 2, \"dominant_damage\": \"no-damage\", \"major_damage\": 1, \"minor_damage\": 7, \"no_damage\": 10, \"row\": 0, \"severe_area_m2\": 11824.1585, \"severe_count\": 3, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=162e1b9cf503]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 0.75, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 0.75, \"concentration_status\": \"scored\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 3, \"per_quadrant_severe\": {\"Q0\": 1, \"Q1\": 3, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [\"Q1\"], \"total_severe\": 4, \"worst_quadrant\": \"Q1\"}, \"score_1_payloa...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1681, "id": "xbd_damage/hurricane-michael_00000077/XBD-Q7", "dataset_id": "xbd_damage/hurricane-michael_00000077/XBD-Q7", "sample_id": "hurricane-michael_00000077", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "162e1b9cf503db8685c7ea7cbea8307104f2aabf146760f43bef0c5bab04fbc6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 2, \"sample_objects\": [{\"area_m2\": 8336.4284, \"centroid_x\": 425.2638, \"centroid_y\": 912.8601, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 1146.712, \"centroid_x\": 454.0218, \"centroid_y\": 775.0606, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"tar...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 2, \"sample_objects\": [{\"area_m2\": 8336.4284, \"centroid_x\": 425.2638, \"centroid_y\": 912.8601, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 1146.712, \"centroid_x\": 454.0218, \"centroid_y\": 775.0606, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 9483.14}, \"objects_1_payload\": {\"object_count\": 2, \"sample_objects\": [{\"area_m2\": 8336.4284, \"centroid_x\": 425.2638, \"centroid_y\": 912.8601, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-matthew), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 2, \"severe_footprint_m2\": 9483.1404}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 2, \"sample_objects\": [{\"area_m2\": 8336.4284, \"centroid_x\": 425.2638, \"centroid_y\": 912.8601, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}, {\"area_m2\": 1146.712, \"centroid_x\": 454.0218, \"centroid_y\": 775.0606, \"damage_score\": 2, \"is_severe\": true, \"subtype\": \"major-damage\"}], \"tar...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=78f6f2414c64]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 2, \"severe_footprint_m2\": 9483.1404}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 9483.1404, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [{\"area_m2\": 8336.4284, \"centroid_x\": 425.2638, \"centroid_y\": 912.8601, \"damage_score\": 2, \"subtype\": \"major-damage\"}, {\"area_m2\": 1146.712, \"centroid_x\": 454.0218, \"centroid_y\": 775.0606, \"damage_score\": 2, \"subtype\": \"major-damage\"}], \"severe_building_count\": 2, \"severe_count\": 2, \"severe_footprint_m2\": 9483.1404}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_sou...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1710, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q5", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q5", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "78f6f2414c648dbf575dfe904124b8d0e26e4df4199d09dc35d9874362870f66"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2, \"severe_pct\": 7.4074, \"severity_score\": 0.345679, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"unclassified\": 1}, \"total_area_m2\": 94171.0166, \"unclassified\": 1}, {\"building_count\": 30, \"cell_id\": \"Q0\", \"classified_building_count\": 23, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 0, \"minor_damage\": 19, \"no_damage\": 4, \"row\": 0, \"severe_area_m2\": 0.0, \"seve...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2, \"severe_pct\": 7.4074, \"severity_score\": 0.345679, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"unclassified\": 1}, \"total_area_m2\": 94171.0166, \"unclassified\": 1}, {\"building_count\": 30, \"cell_id\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 28, \"cell_id\": \"Q2\", \"classified_building_count\": 27, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"minor-damage\", \"major_damage\": 2, \"minor_damage\": 24, \"no_damage\": 1, \"row\": 1, \"severe_area_m2\": 9483.1404, \"severe_count\": 2,...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=21d54e7fa5b9]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"highly concentrated\", \"concentration_score\": 1.0, \"concentration_status\": \"scored\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 2, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 2, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [\"Q2\"], \"total_severe\": 2, \"worst_quadrant\": \"Q2\"}, \"score_1_payload_...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1711, "id": "xbd_damage/hurricane-matthew_00000073/XBD-Q7", "dataset_id": "xbd_damage/hurricane-matthew_00000073/XBD-Q7", "sample_id": "hurricane-matthew_00000073", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "21d54e7fa5b97a3fbc4f6d73c661a5e887c15361f28925a17bf6c82ce526998a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload\": {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}, \"objects_1_payload_keys\": [\"target\", \"object_count\", \"total_area_m2\", \"sample_objects\"], \"objects_1_summary\": \"extracted objects `objects_1`\"}, \"layers\": []}, \"text\": \"extracted objects `objects_1`\", \"tool\": \"ExtractObjects\"}\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster flooding scene (hurricane-harvey), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 0, \"sample_objects\": [], \"target\": \"severe_buildings\", \"total_area_m2\": 0}\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1e05b2ca7abf]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 0, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 0, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [], \"severe_building_count\": 0, \"severe_count\": 0, \"severe_footprint_m2\": 0}, \"damage_1_payload_keys\": [\"aggregation\", \"filter\", \"polygon_source\", \"severe_count\", \"severe_building_count\", \"severe_footprint_m2\", \"major_damage_area_m2\", \"destroyed_area_m2\", \"area_method\", \"sample_severe_buildings\"], \"damage_1_summary\": \"assessed damage `damage_1`\", \"latest_answer_ref\": \"ANSWER_JSON:damage_1\", \"lat...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1716, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q5", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q5", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 0.14285714, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1e05b2ca7abf828d1c0005b40536473cc9d27580d79226f504a28d685018087a"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"unclassified\": 0}, \"total_area_m2\": 839314.6476, \"unclassified\": 0}, {\"building_count\": 29, \"cell_id\": \"Q1\", \"classified_building_count\": 29, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 29, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"seve...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_pct\": 0.0, \"severity_score\": 0.0, \"subtype_histogram\": {\"destroyed\": 0, \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"unclassified\": 0}, \"total_area_m2\": 839314.6476, \"unclassified\": 0}, {\"building_count\": 29, \"cell_id\": \"Q1\", \"classifie...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_co...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 12, \"cell_id\": \"Q0\", \"classified_building_count\": 12, \"col\": 0, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 0, \"minor_damage\": 0, \"no_damage\": 12, \"row\": 0, \"severe_area_m2\": 0.0, \"severe_count\": 0, \"severe_p...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5f9282bf333]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"no_severe_damage\", \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"no_severe_damage\", \"concentration_score\": null, \"concentration_status\": \"no_severe_damage\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 0, \"per_quadrant_severe\": {\"Q0\": 0, \"Q1\": 0, \"Q2\": 0, \"Q3\": 0}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [], \"total_severe\": 0, \"worst_quadrant\": null}, \"score_1_payload_keys\": [\"hazard\", \"scale\",...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1717, "id": "xbd_damage/hurricane-harvey_00000016/XBD-Q7", "dataset_id": "xbd_damage/hurricane-harvey_00000016/XBD-Q7", "sample_id": "hurricane-harvey_00000016", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5f9282bf3334e4c076f37b569638565b040afdfeb4492741d94d1d23510614f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_damage` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_damage\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_damage\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `severe_buildings` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"target\":\"severe_buildings\",\"filters\":{\"severity\":\"major_or_destroyed\"}}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"object_count\": 32, \"sample_objects\": [{\"area_m2\": 1093.9068, \"centroid_x\": 486.5, \"centroid_y\": 251.5, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 549.0983, \"centroid_x\": 496.0, \"centroid_y\": 271.0, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"severe_bui...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"filters\": {\"severity\": \"major_or_destroyed\"}, \"question\": \"question_1\", \"target\": \"severe_buildings\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"object_count\": 32, \"sample_objects\": [{\"area_m2\": 1093.9068, \"centroid_x\": 486.5, \"centroid_y\": 251.5, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 549.0983, \"centroid_x\": 496.0, \"centroid_y\": 271.0, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"severe_buildings\", \"total_area_m2\": 160816.5}, \"objects_1_payload\": {\"object_count\": 32, \"sample_objects\": [{\"area_m2\": 1093.9068, \"centroid_x\": 486.5, \"centroid_y\": 251.5, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 54...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `severe_only` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"aggregation\":\"severe_only\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nFrom the post-disaster wind scene (hurricane-michael), identify all buildings with severe damage (major-damage or destroyed). Report the total severe-building count, combined footprint area, and separate major/destroyed areas in square metres, computed from xy polygon coordinates and the scene GSD.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q5 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q5: prepare object-level spatial evidence from image_1, damage_labels_1. Verify the image/label references and expose the requested objects or components for downstream semantic assessment.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"xBD building-damage / XBD-Q5: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"severe_only\", \"severe_count\": 32, \"severe_footprint_m2\": 160816.4999}\n- damage_1_summary = \"assessed damage `damage_1`\"\n- objects_1_evidence = {\"object_count\": 32, \"sample_objects\": [{\"area_m2\": 1093.9068, \"centroid_x\": 486.5, \"centroid_y\": 251.5, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}, {\"area_m2\": 549.0983, \"centroid_x\": 496.0, \"centroid_y\": 271.0, \"damage_score\": 3, \"is_severe\": true, \"subtype\": \"destroyed\"}], \"target\": \"severe_bui...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fae02dac6244]\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"severe_only\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"severe_only\", \"severe_count\": 32, \"severe_footprint_m2\": 160816.4999}, \"damage_1_payload\": {\"aggregation\": \"severe_only\", \"area_method\": \"xy_polygon_area_times_gsd_squared\", \"destroyed_area_m2\": 16423.248, \"filter\": \"subtype in [major-damage, destroyed]\", \"major_damage_area_m2\": 144393.2519, \"polygon_source\": \"building_damage_xy.geojson\", \"sample_severe_buildings\": [{\"area_m2\": 1093.9068, \"centroid_x\": 486.5, \"centroid_y\": 251.5, \"damage_score\": 3, \"subtype\": \"destroyed\"}, {\"area_m2\": 549.0983, \"centroid_x\": 496.0, \"centroid_y\": 271.0, \"damage_score\": 3, \"subtype\": \"destroyed\"}, {\"area_m2\": 2769.0856, \"centroid_x\": 557.8518, \"centroid_y\": 247.8663, \"damage_score\": 2, \"subtype\": \"major-damage\"}, {\"area_m2\": 270.2593, \"centroid_x...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1800, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q5", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q5", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q5", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "ExtractObjects", "AssessDamage"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "fae02dac62441eb3830dc712e241894692d5e8948905edd4fc741203ae82d19b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\nREGISTERED INPUTS\n- damage_labels_1 [damage_labels] - Building damage polygons and region grids.\n- image_1 [post_image] - Post-disaster xBD image.\n- question_1 [question] - Current natural-language question and schema handle.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"image_1\",\"damage_labels_1\"]},{\"id\":\"s2\",\"objective\":\"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\",\"needs\":[\"question_1\",\"damage_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `image_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"image_1\",\"role\":\"post_disaster\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"image_1\", \"question\": \"question_1\", \"role\": \"post_disaster\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `damage_labels_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"damage_labels_1\",\"role\":\"buildings\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"damage_labels_1\", \"question\": \"question_1\", \"role\": \"buildings\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"image\\\": \\\"scene_1\\\", \\\"labels\\\": \\\"scene_2\\\"} into a `xbd_quadrants` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"image\":\"scene_1\",\"labels\":\"scene_2\"},\"mode\":\"xbd_quadrants\",\"checks\":{\"grid\":\"2x2\"}}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"2x2\"}, \"inputs\": {\"image\": \"scene_1\", \"labels\": \"scene_2\"}, \"mode\": \"xbd_quadrants\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Damage evidence is prepared; aggregate by `quadrant_2x2` with severity `major_or_destroyed`.\",\"action\":{\"tool\":\"AssessDamage\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"aggregation\":\"quadrant_2x2\",\"severity\":\"major_or_destroyed\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"assessed damage `damage_1`\"\n- latest_handle = \"damage_1\"\n- latest_tool = \"AssessDamage\"\n- latest_tool_ok = true\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"aggregation\": \"quadrant_2x2\", \"question\": \"question_1\", \"severity\": \"major_or_destroyed\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"damage_1_answer_ref\": \"ANSWER_JSON:damage_1\", \"damage_1_evidence\": {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"severe_pct\": 47.619, \"severity_score\": 0.492063, \"subtype_histogram\": {\"destroyed\": 6, \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 123089.0598, \"unclassified\": 0}, {\"building_count\": 33, \"cell_id\": \"Q1\", \"classified_building_count\": 33, \"col\": 1, \"destroyed\": 0, \"dominant_damage\": \"no-damage\", \"major_damage\": 9, \"minor_damage\": 4, \"no_damage\": 20, \"row\": 0, \"severe_area_m2\": 31064.7134, \"se...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `quadrants_2x2` by `severe_damage_count` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"damage_1\",\"by\":\"severe_damage_count\",\"candidates\":\"quadrants_2x2\",\"top_k\":4}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"sev...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"question\": \"question_1\", \"top_k\": 4, \"work\": \"damage_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"severe_pct\": 47.619, \"severity_score\": 0.492063, \"subtype_histogram\": {\"destroyed\": 6, \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"unclassified\": 0}, \"total_area_m2\": 123089.0598, \"unclassified\": 0}, {\"building_count\": 33, \"cell_id\": \"Q...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Hazard evidence is prepared; score `building_damage` on `spatial_concentration` and keep support.\",\"action\":{\"tool\":\"ScoreHazard\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"hazard\":\"building_damage\",\"scale\":\"spatial_concentration\"}}}"}, {"role": "user", "content": "QUESTION\nAnalyze severe building damage (major-damage or destroyed) across the 2x2 quadrant grid. Report per-quadrant severe counts, concentration as the largest quadrant count divided by total severe count, the classification, and every quadrant tied for the maximum. If total severe count is zero, report concentration null and no_severe_damage.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the xBD building-damage / XBD-Q7 task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"image_1\", \"damage_labels_1\"], \"objective\": \"xBD building-damage / XBD-Q7: prepare reusable evidence from image_1, damage_labels_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"xBD building-damage / XBD-Q7: interpret prepared damage evidence. Report the requested counts, severity distribution, severe-area support, grid evidence, or quadrant evidence.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"damage_1\"], \"objective\": \"xBD building-damage / XBD-Q7: score the requested hazard/severity level from prepared evidence. Report the class and its supporting fraction or value.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- damage_labels_1 [damage_labels] - damage_labels_1: Building damage polygons and region grids.\n- image_1 [post_image] - image_1: Post-disaster xBD image.\n- question_1 [question] - question_1: Current natural-language question and schema handle.\nFields:\n- latest_summary = \"scored hazard `score_1`\"\n- latest_handle = \"score_1\"\n- latest_tool = \"ScoreHazard\"\n- latest_tool_ok = true\n- score_1_evidence = {\"assessment\": \"spatially dispersed\", \"concentration_score\": 0.3125, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}\n- score_1_summary = \"scored hazard `score_1`\"\n- rank_1_evidence = {\"by\": \"severe_damage_count\", \"candidates\": \"quadrants_2x2\", \"top_k\": 4, \"top_ranked\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"sev...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- damage_1_evidence = {\"aggregation\": \"quadrant_2x2\", \"cell_count\": 4, \"top_severe_cells\": [{\"building_count\": 21, \"cell_id\": \"Q0\", \"classified_building_count\": 21, \"col\": 0, \"destroyed\": 6, \"dominant_damage\": \"destroyed\", \"major_damage\": 4, \"minor_damage\": 5, \"no_damage\": 6, \"row\": 0, \"severe_area_m2\": 92049.3522, \"severe_count\": 10, \"s...\n- damage_1_summary = \"assessed damage `damage_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b2ec2090f6ef]\n\nLAST OBSERVATION\n{\"args\": {\"hazard\": \"building_damage\", \"question\": \"question_1\", \"scale\": \"spatial_concentration\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:score_1\", \"latest_handle\": \"score_1\", \"latest_summary\": \"scored hazard `score_1`\", \"latest_tool\": \"ScoreHazard\", \"latest_tool_ok\": true, \"score_1_answer_ref\": \"ANSWER_JSON:score_1\", \"score_1_evidence\": {\"assessment\": \"spatially dispersed\", \"concentration_score\": 0.3125, \"hazard\": \"building_damage\", \"scale\": \"spatial_concentration\"}, \"score_1_payload\": {\"assessment\": \"spatially dispersed\", \"concentration_score\": 0.3125, \"concentration_status\": \"scored\", \"grid_used\": \"quadrant_2x2\", \"hazard\": \"building_damage\", \"max_severe_in_cell\": 10, \"per_quadrant_severe\": {\"Q0\": 10, \"Q1\": 9, \"Q2\": 9, \"Q3\": 4}, \"scale\": \"spatial_concentration\", \"tie_break_applied\": false, \"tie_break_policy\": \"ascending_cell_id\", \"top_tied_quadrant_ids\": [\"Q0\"], \"total_severe\": 32, \"worst_quadrant\": \"Q0\"}, \"score_1...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "geodisaster", "export_source": "geodisaster_test", "idx": 1801, "id": "xbd_damage/hurricane-michael_00000162/XBD-Q7", "dataset_id": "xbd_damage/hurricane-michael_00000162/XBD-Q7", "sample_id": "hurricane-michael_00000162", "question_type": "XBD-Q7", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "AssessDamage", "RankRegions", "ScoreHazard"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b2ec2090f6ef9290016ea5b6472ea041f5db3b66e0f26680dc245574f2b18ef7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 150, \"edge_count\": 708, \"max_risk\": 1.0, \"mean_risk\": 0.2351, \"risk_buffer_m\": 120, \"risky_edges\": 186, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 150, \"edge_count\": 708, \"max_risk\": 1.0, \"mean_risk\": 0.2351, \"risk_buffer_m\": 120, \"risky_edges\": 186, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 150, \"edge_count\": 708, \"max_risk\": 1.0, \"mean_risk\": 0.2351, \"risk_buffer_m\": 120, \"risky_edges\": 186, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000025/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 150, \"edge_count\": 708, \"max_risk\": 1.0, \"mean_risk\": 0.2351, \"risk_buffer_m\": 120, \"risky_edges\": 186, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2be109f3f0d0]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}}}, \"r...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1894, "id": "routing/000025/nearest_shelter", "dataset_id": "routing/000025/nearest_shelter", "sample_id": "000025", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2be109f3f0d0f5b3b1eb6157edb9594c13aadb61c2bb4a04a3a06a7035b78704"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d71f57919497]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}}}, \"routes_1_paylo...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"delta\": {\"balanced_extra_distance_m\": 227.89, \"balanced_extra_eta_min\": 3.04, \"balanced_risk_reduction\": 0.0901, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2ce37b2a0646]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"delta\": {\"balanced_extra_distance_m\": 227.89, \"balanced_extra_eta_min\": 3.04, \"balanced_risk_reduction\": 0.0901, \"safest_extra_distance_m\": 227.89, \"safest_extra_eta_min\": 3.04, \"safest_risk_reduction\": 0.0901}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1895, "id": "routing/000025/fastest_safest", "dataset_id": "routing/000025/fastest_safest", "sample_id": "000025", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2ce37b2a06462074604fc765ed605930ff15a826920fb089d839a0c2aa99e2b0"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}}}, \"routes_1_payload...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"delta\": {\"balanced_extra_distance_m\": 227.89, \"balanced_extra_eta_min\": 3.04, \"balanced_risk_reduction\": 0.0901, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"delta\": {\"balanced_extra_distance_m\": 227.89, \"balanced_extra_eta_min\": 3.04, \"balanced_risk_reduction\": 0.0901, \"safest_extra_distance_m\": 227.89, \"safest_extra_eta_min\": 3.04, \"safest_risk_reduction\": 0.0901}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1896_afa1efe1a2b6__6a52e18914/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"delta\": {\"balanced_extra_distance_m\": 227.89, \"balanced_extra_eta_min\": 3.04, \"balanced_risk_reduction\": 0.0901, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.747, \"eta_min\": 36.63, \"risk_score\": 0.3601, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Δημήτριος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.519, \"eta_min\": 33.59, \"risk_score\": 0.4502, \"route_id\": \"F1\", \"route_type...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1896_afa1efe1a2b6__6a52e18914/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1896_afa1efe1a2b6__6a52e18914/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1896, "id": "routing/000025/visual_overlay", "dataset_id": "routing/000025/visual_overlay", "sample_id": "000025", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 132, \"edge_count\": 1376, \"max_risk\": 1.0, \"mean_risk\": 0.1393, \"risk_buffer_m\": 120, \"risky_edges\": 240, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 132, \"edge_count\": 1376, \"max_risk\": 1.0, \"mean_risk\": 0.1393, \"risk_buffer_m\": 120, \"risky_edges\": 240, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 132, \"edge_count\": 1376, \"max_risk\": 1.0, \"mean_risk\": 0.1393, \"risk_buffer_m\": 120, \"risky_edges\": 240, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000028/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 132, \"edge_count\": 1376, \"max_risk\": 1.0, \"mean_risk\": 0.1393, \"risk_buffer_m\": 120, \"risky_edges\": 240, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ee87f008778]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Ιωάν...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1903, "id": "routing/000028/nearest_shelter", "dataset_id": "routing/000028/nearest_shelter", "sample_id": "000028", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "6ee87f0087780d77d2450a9adbb17cae4a3a2ddc7871266da9680936b6872da3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=434786f5b5af]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=434786f5b5af]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"route_type\": \"fastest...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1904, "id": "routing/000028/fastest_safest", "dataset_id": "routing/000028/fastest_safest", "sample_id": "000028", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "434786f5b5aff027239e1bc740687274ed654b2c501c43c294a45815060ec54e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"route_type\": \"fastest...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1905_2cc65205b29d__a461862113/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extr...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Άγιος Ιωάννης Πρόδρομος\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.91, \"eta_min\": 12.13, \"risk_score\": 0.3415, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1905_2cc65205b29d__a461862113/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1905_2cc65205b29d__a461862113/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1905, "id": "routing/000028/visual_overlay", "dataset_id": "routing/000028/visual_overlay", "sample_id": "000028", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 54, \"edge_count\": 1474, \"max_risk\": 1.0, \"mean_risk\": 0.0747, \"risk_buffer_m\": 120, \"risky_edges\": 158, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 54, \"edge_count\": 1474, \"max_risk\": 1.0, \"mean_risk\": 0.0747, \"risk_buffer_m\": 120, \"risky_edges\": 158, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 54, \"edge_count\": 1474, \"max_risk\": 1.0, \"mean_risk\": 0.0747, \"risk_buffer_m\": 120, \"risky_edges\": 158, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000036/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 54, \"edge_count\": 1474, \"max_risk\": 1.0, \"mean_risk\": 0.0747, \"risk_buffer_m\": 120, \"risky_edges\": 158, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e906cec124b1]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelt...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1927, "id": "routing/000036/nearest_shelter", "dataset_id": "routing/000036/nearest_shelter", "sample_id": "000036", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e906cec124b1b85b06b693743617b1f74215c5522cb473d4b3ccccef8b3d147f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8de0a8de1f88]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Ma...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8de0a8de1f88]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"F1\", \"route_type\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1928, "id": "routing/000036/fastest_safest", "dataset_id": "routing/000036/fastest_safest", "sample_id": "000036", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8de0a8de1f88f2bac635d50faffab5d3499368c605714227a080f33f295fdd60"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Mair...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"F1\", \"route_type\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1929_1331cfd3e72d__21de99936a/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Mairie de Saint-Michel-de-Bannières\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.41, \"eta_min\": 18.8, \"risk_score\": 0.1784, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1929_1331cfd3e72d__21de99936a/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1929_1331cfd3e72d__21de99936a/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1929, "id": "routing/000036/visual_overlay", "dataset_id": "routing/000036/visual_overlay", "sample_id": "000036", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 242, \"edge_count\": 1434, \"max_risk\": 1.0, \"mean_risk\": 0.2406, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 242, \"edge_count\": 1434, \"max_risk\": 1.0, \"mean_risk\": 0.2406, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 242, \"edge_count\": 1434, \"max_risk\": 1.0, \"mean_risk\": 0.2406, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000051/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 242, \"edge_count\": 1434, \"max_risk\": 1.0, \"mean_risk\": 0.2406, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f2322b4428d4]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Capela São João\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Capela São João\"}}}, \"rout...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1972, "id": "routing/000051/nearest_shelter", "dataset_id": "routing/000051/nearest_shelter", "sample_id": "000051", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f2322b4428d41bc4ef59b734d3deaab3afd53ae8c7a7b7297fef30a47ab7fcf1"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a4d26a3b155d]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Capela São João\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Capela São João\"}}}, \"routes_1_payload\"...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distan...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a4d26a3b155d]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"sele...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1973, "id": "routing/000051/fastest_safest", "dataset_id": "routing/000051/fastest_safest", "sample_id": "000051", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a4d26a3b155dbe733ae6d35bba0b139658ff5541f6d60cbe21151069e13494a8"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Capela São João\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Capela São João\"}}}, \"routes_1_payload\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distan...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"sele...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1974_35b0591c22c8__5546c42673/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distan...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Capela São João\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.475, \"eta_min\": 6.34, \"risk_score\": 0.1884, \"route_id\": \"F1\", \"route_type\":...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1974_35b0591c22c8__5546c42673/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1974_35b0591c22c8__5546c42673/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1974, "id": "routing/000051/visual_overlay", "dataset_id": "routing/000051/visual_overlay", "sample_id": "000051", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 256, \"edge_count\": 3026, \"max_risk\": 1.0, \"mean_risk\": 0.1114, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 256, \"edge_count\": 3026, \"max_risk\": 1.0, \"mean_risk\": 0.1114, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 256, \"edge_count\": 3026, \"max_risk\": 1.0, \"mean_risk\": 0.1114, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000054/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 256, \"edge_count\": 3026, \"max_risk\": 1.0, \"mean_risk\": 0.1114, \"risk_buffer_m\": 120, \"risky_edges\": 446, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fea4c0caf163]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"S1\", \"route_type\": \"safest\", \"select...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1981, "id": "routing/000054/nearest_shelter", "dataset_id": "routing/000054/nearest_shelter", "sample_id": "000054", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "fea4c0caf163caba617d60e17c6e6640723b2c78c9a6e6ce98eb0fb22d6601d6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b502be863ccb]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_na...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b502be863ccb]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"F1\", \"route_...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1982, "id": "routing/000054/fastest_safest", "dataset_id": "routing/000054/fastest_safest", "sample_id": "000054", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b502be863ccb59b142e5efdc2e8ec22b18e6404105f5e7fad9619aa72c7adf29"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"F1\", \"route_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1983_9a5ca4d4fcbd__6745556697/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Iglesia del Sagrado Corazón de Jesús\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.999, \"eta_min\": 13.31, \"risk_score\": 0.3586, \"route_i...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1983_9a5ca4d4fcbd__6745556697/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1983_9a5ca4d4fcbd__6745556697/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1983, "id": "routing/000054/visual_overlay", "dataset_id": "routing/000054/visual_overlay", "sample_id": "000054", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 226, \"edge_count\": 3528, \"max_risk\": 1.0, \"mean_risk\": 0.0876, \"risk_buffer_m\": 120, \"risky_edges\": 400, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 226, \"edge_count\": 3528, \"max_risk\": 1.0, \"mean_risk\": 0.0876, \"risk_buffer_m\": 120, \"risky_edges\": 400, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 226, \"edge_count\": 3528, \"max_risk\": 1.0, \"mean_risk\": 0.0876, \"risk_buffer_m\": 120, \"risky_edges\": 400, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000056/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 226, \"edge_count\": 3528, \"max_risk\": 1.0, \"mean_risk\": 0.0876, \"risk_buffer_m\": 120, \"risky_edges\": 400, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ceba8c986a1a]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_score\": 0.1376, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343,...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1987, "id": "routing/000056/nearest_shelter", "dataset_id": "routing/000056/nearest_shelter", "sample_id": "000056", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ceba8c986a1a828529e52d37242a4572fbc684fe838d98521852bd3feb592cc7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c05019e6c7f]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_score\": 0.1376, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"delta\": {\"balanced_extra_distance_m\": 5.69, \"balanced_extra_eta_min\": 0.08, \"ba...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3dcc5352617d]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"delta\": {\"balanced_extra_distance_m\": 5.69, \"balanced_extra_eta_min\": 0.08, \"balanced_risk_reduction\": 0.0033, \"safest_extra_distance_m\": 5.69, \"safest_extra_eta_min\": 0.08, \"safest_risk_reduction\": 0.0033}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_score\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1988, "id": "routing/000056/fastest_safest", "dataset_id": "routing/000056/fastest_safest", "sample_id": "000056", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3dcc5352617df49225b12fb0d95330f425a05b3881bba504d448db59e42d4087"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_score\": 0.1376, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"S1...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"delta\": {\"balanced_extra_distance_m\": 5.69, \"balanced_extra_eta_min\": 0.08, \"ba...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"delta\": {\"balanced_extra_distance_m\": 5.69, \"balanced_extra_eta_min\": 0.08, \"balanced_risk_reduction\": 0.0033, \"safest_extra_distance_m\": 5.69, \"safest_extra_eta_min\": 0.08, \"safest_risk_reduction\": 0.0033}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_score\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1989_c20bd64b04be__b8402570c5/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"delta\": {\"balanced_extra_distance_m\": 5.69, \"balanced_extra_eta_min\": 0.08, \"ba...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.736, \"eta_min\": 9.82, \"risk_score\": 0.1343, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Colegio de Educación Infantil y Primaria Federico García Lorca\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.731, \"eta_min\": 9.74, \"risk_...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1989_c20bd64b04be__b8402570c5/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_1989_c20bd64b04be__b8402570c5/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 1989, "id": "routing/000056/visual_overlay", "dataset_id": "routing/000056/visual_overlay", "sample_id": "000056", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 70, \"edge_count\": 442, \"max_risk\": 1.0, \"mean_risk\": 0.2426, \"risk_buffer_m\": 120, \"risky_edges\": 142, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 70, \"edge_count\": 442, \"max_risk\": 1.0, \"mean_risk\": 0.2426, \"risk_buffer_m\": 120, \"risky_edges\": 142, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 70, \"edge_count\": 442, \"max_risk\": 1.0, \"mean_risk\": 0.2426, \"risk_buffer_m\": 120, \"risky_edges\": 142, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000079/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 70, \"edge_count\": 442, \"max_risk\": 1.0, \"mean_risk\": 0.2426, \"risk_buffer_m\": 120, \"risky_edges\": 142, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=bf1ec299c455]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"school\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"school\"}}}, \"routes_1_payload\": {\"avoid\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2056, "id": "routing/000079/nearest_shelter", "dataset_id": "routing/000079/nearest_shelter", "sample_id": "000079", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "bf1ec299c4559ba11160d8aaa901b054344c4c500f2c94a0dfe8ae9a973d0447"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=208d89da6038]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"school\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"school\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=208d89da6038]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_sh...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2057, "id": "routing/000079/fastest_safest", "dataset_id": "routing/000079/fastest_safest", "sample_id": "000079", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "208d89da6038ce720dcac7c9d93091ce3764aedb09359c5da60b0d254c312105"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"school\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"school\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affect...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_sh...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2058_936c4735a9d4__34d11703f6/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"school\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.538, \"eta_min\": 47.17, \"risk_score\": 0.2884, \"route_id\": \"F1\", \"route_type\": \"faste...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2058_936c4735a9d4__34d11703f6/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2058_936c4735a9d4__34d11703f6/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2058, "id": "routing/000079/visual_overlay", "dataset_id": "routing/000079/visual_overlay", "sample_id": "000079", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 22, \"edge_count\": 1876, \"max_risk\": 1.0, \"mean_risk\": 0.0208, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 22, \"edge_count\": 1876, \"max_risk\": 1.0, \"mean_risk\": 0.0208, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 22, \"edge_count\": 1876, \"max_risk\": 1.0, \"mean_risk\": 0.0208, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000081/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 22, \"edge_count\": 1876, \"max_risk\": 1.0, \"mean_risk\": 0.0208, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3b3590d5e13c]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2062, "id": "routing/000081/nearest_shelter", "dataset_id": "routing/000081/nearest_shelter", "sample_id": "000081", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3b3590d5e13c801e9f3b64b5acf8c494629f15a3b77451cda0efd97fe299523f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88c4defaea58]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_p...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9591bbb98a69]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_extra_distance_m\": 1862.74, \"safest_extra_eta_min\": 24.84, \"safest_risk_reduction\": 0.1593}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_ty...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2063, "id": "routing/000081/fastest_safest", "dataset_id": "routing/000081/fastest_safest", "sample_id": "000081", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 0.5, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9591bbb98a691a46ddd03cd8b7e7871757e60cab213fc94d789580c60d9146e1"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_pay...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_extra_distance_m\": 1862.74, \"safest_extra_eta_min\": 24.84, \"safest_risk_reduction\": 0.1593}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_ty...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2064_70c93c637e83__e3e678fdfd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2064_70c93c637e83__e3e678fdfd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2064_70c93c637e83__e3e678fdfd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2064, "id": "routing/000081/visual_overlay", "dataset_id": "routing/000081/visual_overlay", "sample_id": "000081", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 22, \"edge_count\": 1482, \"max_risk\": 1.0, \"mean_risk\": 0.0263, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 22, \"edge_count\": 1482, \"max_risk\": 1.0, \"mean_risk\": 0.0263, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 22, \"edge_count\": 1482, \"max_risk\": 1.0, \"mean_risk\": 0.0263, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000083/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 22, \"edge_count\": 1482, \"max_risk\": 1.0, \"mean_risk\": 0.0263, \"risk_buffer_m\": 120, \"risky_edges\": 52, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3b3590d5e13c]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2068, "id": "routing/000083/nearest_shelter", "dataset_id": "routing/000083/nearest_shelter", "sample_id": "000083", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3b3590d5e13c801e9f3b64b5acf8c494629f15a3b77451cda0efd97fe299523f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88c4defaea58]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_p...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9591bbb98a69]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_extra_distance_m\": 1862.74, \"safest_extra_eta_min\": 24.84, \"safest_risk_reduction\": 0.1593}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_ty...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2069, "id": "routing/000083/fastest_safest", "dataset_id": "routing/000083/fastest_safest", "sample_id": "000083", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 0.5, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9591bbb98a691a46ddd03cd8b7e7871757e60cab213fc94d789580c60d9146e1"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_pay...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_extra_distance_m\": 1862.74, \"safest_extra_eta_min\": 24.84, \"safest_risk_reduction\": 0.1593}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_ty...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2070_01f647645e2f__7d5557d6bd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 1862.74, \"balanced_extra_eta_min\": 24.84, \"balanced_risk_reduction\": 0.1593, \"safest_...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 8.173, \"eta_min\": 108.97, \"risk_score\": 0.0532, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.31, \"eta_min\": 84.13, \"risk_score\": 0.2125, \"route_id\": \"F1\", \"route_typ...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2070_01f647645e2f__7d5557d6bd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2070_01f647645e2f__7d5557d6bd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2070, "id": "routing/000083/visual_overlay", "dataset_id": "routing/000083/visual_overlay", "sample_id": "000083", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 50, \"edge_count\": 20342, \"max_risk\": 1.0, \"mean_risk\": 0.0047, \"risk_buffer_m\": 120, \"risky_edges\": 144, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 50, \"edge_count\": 20342, \"max_risk\": 1.0, \"mean_risk\": 0.0047, \"risk_buffer_m\": 120, \"risky_edges\": 144, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 50, \"edge_count\": 20342, \"max_risk\": 1.0, \"mean_risk\": 0.0047, \"risk_buffer_m\": 120, \"risky_edges\": 144, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000120/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 50, \"edge_count\": 20342, \"max_risk\": 1.0, \"mean_risk\": 0.0047, \"risk_buffer_m\": 120, \"risky_edges\": 144, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ec7ee905cda3]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"S1\", \"route_type\":...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2179, "id": "routing/000120/nearest_shelter", "dataset_id": "routing/000120/nearest_shelter", "sample_id": "000120", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ec7ee905cda3f2b5418c2c905ae69f71639c59a28a98700a716748dcb2b82a92"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=18ca845a64ff]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"S1\", \"route_type\": \"safest\", \"s...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduct...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=18ca845a64ff]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"F1\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2180, "id": "routing/000120/fastest_safest", "dataset_id": "routing/000120/fastest_safest", "sample_id": "000120", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "18ca845a64ffa37bbb32976efbac86078777bafdcbdb204a94af0322cf6220ae"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"S1\", \"route_type\": \"safest\", \"sel...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduct...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"F1\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2181_0bccc7f508f5__0453d3caf2/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduct...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Chiesa parrocchiale di Santa Maria Immacolata\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.811, \"eta_min\": 10.81, \"risk_score\": 0.2613,...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2181_0bccc7f508f5__0453d3caf2/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2181_0bccc7f508f5__0453d3caf2/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2181, "id": "routing/000120/visual_overlay", "dataset_id": "routing/000120/visual_overlay", "sample_id": "000120", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 106, \"max_risk\": 0.7703, \"mean_risk\": 0.0321, \"risk_buffer_m\": 120, \"risky_edges\": 8, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 0, \"edge_count\": 106, \"max_risk\": 0.7703, \"mean_risk\": 0.0321, \"risk_buffer_m\": 120, \"risky_edges\": 8, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 0, \"edge_count\": 106, \"max_risk\": 0.7703, \"mean_risk\": 0.0321, \"risk_buffer_m\": 120, \"risky_edges\": 8, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000125/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 106, \"max_risk\": 0.7703, \"mean_risk\": 0.0321, \"risk_buffer_m\": 120, \"risky_edges\": 8, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=005a7d6e34e3]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2194, "id": "routing/000125/nearest_shelter", "dataset_id": "routing/000125/nearest_shelter", "sample_id": "000125", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "005a7d6e34e3afa06d3cc240b79e35b11845c290cd30fedc5e7c8a2daae8a1d3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=55a636d85d84]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affecte...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0....\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=55a636d85d84]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelt...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2195, "id": "routing/000125/fastest_safest", "dataset_id": "routing/000125/fastest_safest", "sample_id": "000125", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "55a636d85d84d54c16fbe2b66791e673b40d4d0d69100d7a164f3de1dc3456ef"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affected_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0....\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelt...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2196_6f639a121d52__d30a4d2fcb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0....\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.813, \"eta_min\": 50.83, \"risk_score\": 0.02, \"route_id\": \"F1\", \"route_type\": \"fastest\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2196_6f639a121d52__d30a4d2fcb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2196_6f639a121d52__d30a4d2fcb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2196, "id": "routing/000125/visual_overlay", "dataset_id": "routing/000125/visual_overlay", "sample_id": "000125", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 16, \"edge_count\": 552, \"max_risk\": 1.0, \"mean_risk\": 0.0971, \"risk_buffer_m\": 120, \"risky_edges\": 94, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 16, \"edge_count\": 552, \"max_risk\": 1.0, \"mean_risk\": 0.0971, \"risk_buffer_m\": 120, \"risky_edges\": 94, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 16, \"edge_count\": 552, \"max_risk\": 1.0, \"mean_risk\": 0.0971, \"risk_buffer_m\": 120, \"risky_edges\": 94, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000129/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSW...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 16, \"edge_count\": 552, \"max_risk\": 1.0, \"mean_risk\": 0.0971, \"risk_buffer_m\": 120, \"risky_edges\": 94, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=476546ce54f6]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payloa...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2206, "id": "routing/000129/nearest_shelter", "dataset_id": "routing/000129/nearest_shelter", "sample_id": "000129", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "476546ce54f6f88b4d102211e20fbfa4c8a8fd834d51a497e904552fdace0236"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ac3d60ce2a76]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payload\": {\"avoid\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=67497bcf44b1]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_distance_m\": 634.04, \"safest_extra_eta_min\": 8.46, \"safest_risk_reduction\": 0.3638}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"faste...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2207, "id": "routing/000129/fastest_safest", "dataset_id": "routing/000129/fastest_safest", "sample_id": "000129", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 0.5, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "67497bcf44b145e7c3a8e365993aa77af718da669244c473267dfd2c2000fb0b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payload\": {\"avoid\": {...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_distance_m\": 634.04, \"safest_extra_eta_min\": 8.46, \"safest_risk_reduction\": 0.3638}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"faste...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2208_2838d54410b7__14cc3907dd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2208_2838d54410b7__14cc3907dd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2208_2838d54410b7__14cc3907dd/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2208, "id": "routing/000129/visual_overlay", "dataset_id": "routing/000129/visual_overlay", "sample_id": "000129", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 16, \"edge_count\": 566, \"max_risk\": 1.0, \"mean_risk\": 0.0956, \"risk_buffer_m\": 120, \"risky_edges\": 96, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 16, \"edge_count\": 566, \"max_risk\": 1.0, \"mean_risk\": 0.0956, \"risk_buffer_m\": 120, \"risky_edges\": 96, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 16, \"edge_count\": 566, \"max_risk\": 1.0, \"mean_risk\": 0.0956, \"risk_buffer_m\": 120, \"risky_edges\": 96, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000130/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSW...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 16, \"edge_count\": 566, \"max_risk\": 1.0, \"mean_risk\": 0.0956, \"risk_buffer_m\": 120, \"risky_edges\": 96, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=476546ce54f6]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payloa...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2209, "id": "routing/000130/nearest_shelter", "dataset_id": "routing/000130/nearest_shelter", "sample_id": "000130", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "476546ce54f6f88b4d102211e20fbfa4c8a8fd834d51a497e904552fdace0236"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ac3d60ce2a76]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payload\": {\"avoid\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tj��rnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=67497bcf44b1]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_distance_m\": 634.04, \"safest_extra_eta_min\": 8.46, \"safest_risk_reduction\": 0.3638}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"faste...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2210, "id": "routing/000130/fastest_safest", "dataset_id": "routing/000130/fastest_safest", "sample_id": "000130", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 0.5, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "67497bcf44b145e7c3a8e365993aa77af718da669244c473267dfd2c2000fb0b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.808, \"eta_min\": 24.11, \"risk_score\": 0.1357, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Tjärnstugan\"}}}, \"routes_1_payload\": {\"avoid\": {...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_distance_m\": 634.04, \"safest_extra_eta_min\": 8.46, \"safest_risk_reduction\": 0.3638}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"faste...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2211_81f9f435fc30__81f42e8ffb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"delta\": {\"balanced_extra_distance_m\": 205.86, \"balanced_extra_eta_min\": 2.75, \"balanced_risk_reduction\": 0.2511, \"safest_extra_dist...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.38, \"eta_min\": 18.4, \"risk_score\": 0.2484, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Tjärnstugan\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.174, \"eta_min\": 15.65, \"risk_score\": 0.4995, \"route_id\": \"F1\", \"route_type\": \"fa...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2211_81f9f435fc30__81f42e8ffb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2211_81f9f435fc30__81f42e8ffb/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2211, "id": "routing/000130/visual_overlay", "dataset_id": "routing/000130/visual_overlay", "sample_id": "000130", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 474, \"max_risk\": 0.0, \"mean_risk\": 0.0, \"risk_buffer_m\": 120, \"risky_edges\": 0, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 0, \"edge_count\": 474, \"max_risk\": 0.0, \"mean_risk\": 0.0, \"risk_buffer_m\": 120, \"risky_edges\": 0, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 0, \"edge_count\": 474, \"max_risk\": 0.0, \"mean_risk\": 0.0, \"risk_buffer_m\": 120, \"risky_edges\": 0, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000134/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSWER_JSON:ex...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 474, \"max_risk\": 0.0, \"mean_risk\": 0.0, \"risk_buffer_m\": 120, \"risky_edges\": 0, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b72bb3761132]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"flo...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2221, "id": "routing/000134/nearest_shelter", "dataset_id": "routing/000134/nearest_shelter", "sample_id": "000134", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b72bb37611329614bf7d226e95cf947a7483b85eec8c623a535c55c11fb6ca97"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f5a90f7f8386]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affected_r...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f5a90f7f8386]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2222, "id": "routing/000134/fastest_safest", "dataset_id": "routing/000134/fastest_safest", "sample_id": "000134", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f5a90f7f838649c81ed59a6a5752c6f9c169d4c66fbf72425bb8aa68773b381e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"shelter\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"shelter\"}}}, \"routes_1_payload\": {\"avoid\": {\"flood_affected_roa...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2223_e459562b5c1b__059747cd50/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"shelter\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 3.343, \"eta_min\": 44.58, \"risk_score\": 0.0, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2223_e459562b5c1b__059747cd50/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2223_e459562b5c1b__059747cd50/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2223, "id": "routing/000134/visual_overlay", "dataset_id": "routing/000134/visual_overlay", "sample_id": "000134", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 4, \"edge_count\": 186, \"max_risk\": 1.0, \"mean_risk\": 0.0226, \"risk_buffer_m\": 120, \"risky_edges\": 6, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 4, \"edge_count\": 186, \"max_risk\": 1.0, \"mean_risk\": 0.0226, \"risk_buffer_m\": 120, \"risky_edges\": 6, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 4, \"edge_count\": 186, \"max_risk\": 1.0, \"mean_risk\": 0.0226, \"risk_buffer_m\": 120, \"risky_edges\": 6, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000146/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSWER_J...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 4, \"edge_count\": 186, \"max_risk\": 1.0, \"mean_risk\": 0.0226, \"risk_buffer_m\": 120, \"risky_edges\": 6, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=22435d774b44]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Biserica Ro...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2257, "id": "routing/000146/nearest_shelter", "dataset_id": "routing/000146/nearest_shelter", "sample_id": "000146", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "22435d774b44534c1c3ec4d8d5ddfb358112bd6c9cc9a8ede76e08c811322c79"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=826bb1cc2153]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Biserica Romano-Catolică...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"delta\": {\"balanced_extra_distance_m\": 38.01, \"balanced_extra_eta_min\": 0.51, \"balanced_risk_reduction\": 0.0323, \"safest...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b86ea1450151]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"delta\": {\"balanced_extra_distance_m\": 38.01, \"balanced_extra_eta_min\": 0.51, \"balanced_risk_reduction\": 0.0323, \"safest_extra_distance_m\": 38.01, \"safest_extra_eta_min\": 0.51, \"safest_risk_reduction\": 0.0323}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"route_typ...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2258, "id": "routing/000146/fastest_safest", "dataset_id": "routing/000146/fastest_safest", "sample_id": "000146", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b86ea1450151c204b538a5cb77b9bc796e40831c06fd043f3b55b980b9a566c9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"delta\": {\"balanced_extra_distance_m\": 38.01, \"balanced_extra_eta_min\": 0.51, \"balanced_risk_reduction\": 0.0323, \"safest...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"delta\": {\"balanced_extra_distance_m\": 38.01, \"balanced_extra_eta_min\": 0.51, \"balanced_risk_reduction\": 0.0323, \"safest_extra_distance_m\": 38.01, \"safest_extra_eta_min\": 0.51, \"safest_risk_reduction\": 0.0323}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"route_typ...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2259_ee60e1e39b19__4068c35d13/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"delta\": {\"balanced_extra_distance_m\": 38.01, \"balanced_extra_eta_min\": 0.51, \"balanced_risk_reduction\": 0.0323, \"safest...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.934, \"eta_min\": 12.46, \"risk_score\": 0.0, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Biserica Romano-Catolică\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.896, \"eta_min\": 11.95, \"risk_score\": 0.0323, \"route_id\": \"F1\", \"rout...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2259_ee60e1e39b19__4068c35d13/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2259_ee60e1e39b19__4068c35d13/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2259, "id": "routing/000146/visual_overlay", "dataset_id": "routing/000146/visual_overlay", "sample_id": "000146", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 28, \"edge_count\": 628, \"max_risk\": 1.0, \"mean_risk\": 0.0727, \"risk_buffer_m\": 120, \"risky_edges\": 74, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 28, \"edge_count\": 628, \"max_risk\": 1.0, \"mean_risk\": 0.0727, \"risk_buffer_m\": 120, \"risky_edges\": 74, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 28, \"edge_count\": 628, \"max_risk\": 1.0, \"mean_risk\": 0.0727, \"risk_buffer_m\": 120, \"risky_edges\": 74, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000147/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSW...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 28, \"edge_count\": 628, \"max_risk\": 1.0, \"mean_risk\": 0.0727, \"risk_buffer_m\": 120, \"risky_edges\": 74, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=174169c7d6f2]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"r...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2260, "id": "routing/000147/nearest_shelter", "dataset_id": "routing/000147/nearest_shelter", "sample_id": "000147", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "174169c7d6f2db08ad93ec54b88cec8a6a74465fcff098fe4698abccb7c9f1d5"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5de84da3791f]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_paylo...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_dista...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5de84da3791f]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"sel...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2261, "id": "routing/000147/fastest_safest", "dataset_id": "routing/000147/fastest_safest", "sample_id": "000147", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5de84da3791f959e85b1ffdbab07b608d477fb58f0a65683cccd86c472de1c10"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"place_of_worship\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"place_of_worship\"}}}, \"routes_1_payload...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_dista...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"sel...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2262_220f9a0cea09__05dfb7489e/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_dista...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"place_of_worship\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 2.45, \"eta_min\": 32.67, \"risk_score\": 0.0572, \"route_id\": \"F1\", \"route_type\"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2262_220f9a0cea09__05dfb7489e/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2262_220f9a0cea09__05dfb7489e/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2262, "id": "routing/000147/visual_overlay", "dataset_id": "routing/000147/visual_overlay", "sample_id": "000147", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 7752, \"max_risk\": 0.9143, \"mean_risk\": 0.0091, \"risk_buffer_m\": 120, \"risky_edges\": 174, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 0, \"edge_count\": 7752, \"max_risk\": 0.9143, \"mean_risk\": 0.0091, \"risk_buffer_m\": 120, \"risky_edges\": 174, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 0, \"edge_count\": 7752, \"max_risk\": 0.9143, \"mean_risk\": 0.0091, \"risk_buffer_m\": 120, \"risky_edges\": 174, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000168/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 0, \"edge_count\": 7752, \"max_risk\": 0.9143, \"mean_risk\": 0.0091, \"risk_buffer_m\": 120, \"risky_edges\": 174, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2872d91a031]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelte...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2323, "id": "routing/000168/nearest_shelter", "dataset_id": "routing/000168/nearest_shelter", "sample_id": "000168", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c2872d91a0317e379fe606fe1cb575463646ee17a2bdf0b511d74698a2664d65"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f392f102115d]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Ora...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"s...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f392f102115d]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"F1\", \"route_type\"...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2324, "id": "routing/000168/fastest_safest", "dataset_id": "routing/000168/fastest_safest", "sample_id": "000168", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f392f102115d2e1eb92fc16fb3187abc8ecce9e3fb291be1ec24e3ed98a31dc6"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Orato...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"s...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"F1\", \"route_type\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2325_ed1ec4079fe6__8c23b2281b/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"s...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Oratorio della Madonna della Neve\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 0.431, \"eta_min\": 5.75, \"risk_score\": 0.1562, \"route_id\": \"...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2325_ed1ec4079fe6__8c23b2281b/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2325_ed1ec4079fe6__8c23b2281b/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2325, "id": "routing/000168/visual_overlay", "dataset_id": "routing/000168/visual_overlay", "sample_id": "000168", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 14, \"edge_count\": 9022, \"max_risk\": 1.0, \"mean_risk\": 0.0061, \"risk_buffer_m\": 120, \"risky_edges\": 90, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 14, \"edge_count\": 9022, \"max_risk\": 1.0, \"mean_risk\": 0.0061, \"risk_buffer_m\": 120, \"risky_edges\": 90, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 14, \"edge_count\": 9022, \"max_risk\": 1.0, \"mean_risk\": 0.0061, \"risk_buffer_m\": 120, \"risky_edges\": 90, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000173/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 14, \"edge_count\": 9022, \"max_risk\": 1.0, \"mean_risk\": 0.0061, \"risk_buffer_m\": 120, \"risky_edges\": 90, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2cf13c344eb1]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Cappella...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2338, "id": "routing/000173/nearest_shelter", "dataset_id": "routing/000173/nearest_shelter", "sample_id": "000173", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2cf13c344eb10fe706512a0f814fc543f6b3f1f4dd83447a20147d66d80962e7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0b9c0248bcac]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Cappella di San Jacopo...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"delta\": {\"balanced_extra_distance_m\": 117.03, \"balanced_extra_eta_min\": 1.56, \"balanced_risk_reduction\": 0.5244, \"safe...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5726cb3b8f61]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"delta\": {\"balanced_extra_distance_m\": 117.03, \"balanced_extra_eta_min\": 1.56, \"balanced_risk_reduction\": 0.5244, \"safest_extra_distance_m\": 117.03, \"safest_extra_eta_min\": 1.56, \"safest_risk_reduction\": 0.5244}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"route_...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2339, "id": "routing/000173/fastest_safest", "dataset_id": "routing/000173/fastest_safest", "sample_id": "000173", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5726cb3b8f614e745435d1a4c24507d8ea9e4a43593075a8ceced6281adac981"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"delta\": {\"balanced_extra_distance_m\": 117.03, \"balanced_extra_eta_min\": 1.56, \"balanced_risk_reduction\": 0.5244, \"safe...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"delta\": {\"balanced_extra_distance_m\": 117.03, \"balanced_extra_eta_min\": 1.56, \"balanced_risk_reduction\": 0.5244, \"safest_extra_distance_m\": 117.03, \"safest_extra_eta_min\": 1.56, \"safest_risk_reduction\": 0.5244}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"route_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2340_22430eb95830__f35d876004/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"delta\": {\"balanced_extra_distance_m\": 117.03, \"balanced_extra_eta_min\": 1.56, \"balanced_risk_reduction\": 0.5244, \"safe...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.395, \"eta_min\": 18.61, \"risk_score\": 0.1569, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Cappella di San Jacopo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.278, \"eta_min\": 17.05, \"risk_score\": 0.6813, \"route_id\": \"F1\", \"rou...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2340_22430eb95830__f35d876004/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2340_22430eb95830__f35d876004/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2340, "id": "routing/000173/visual_overlay", "dataset_id": "routing/000173/visual_overlay", "sample_id": "000173", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 2, \"edge_count\": 5220, \"max_risk\": 1.0, \"mean_risk\": 0.0244, \"risk_buffer_m\": 120, \"risky_edges\": 258, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 2, \"edge_count\": 5220, \"max_risk\": 1.0, \"mean_risk\": 0.0244, \"risk_buffer_m\": 120, \"risky_edges\": 258, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 2, \"edge_count\": 5220, \"max_risk\": 1.0, \"mean_risk\": 0.0244, \"risk_buffer_m\": 120, \"risky_edges\": 258, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000176/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"AN...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 2, \"edge_count\": 5220, \"max_risk\": 1.0, \"mean_risk\": 0.0244, \"risk_buffer_m\": 120, \"risky_edges\": 258, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=710c3a983084]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2347, "id": "routing/000176/nearest_shelter", "dataset_id": "routing/000176/nearest_shelter", "sample_id": "000176", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "710c3a98308431538a694128f874c3f167204c160cfabf4875d5c8df97b744b2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a55034663b2c]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0,...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a55034663b2c]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"F1\", \"route_t...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2348, "id": "routing/000176/fastest_safest", "dataset_id": "routing/000176/fastest_safest", "sample_id": "000176", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a55034663b2c03df0b6145b3a0b83b3c4681a2b9a6ff0512efe49b14c4e45045"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\":...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0,...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"F1\", \"route_t...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2349_620258eb237c__d7b2018c54/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0,...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Scuola dell'infanzia di Castelnuovo\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 1.142, \"eta_min\": 15.23, \"risk_score\": 0.2295, \"route_id...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2349_620258eb237c__d7b2018c54/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2349_620258eb237c__d7b2018c54/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2349, "id": "routing/000176/visual_overlay", "dataset_id": "routing/000176/visual_overlay", "sample_id": "000176", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Estimate `blocked_roads` exposure with metrics {\\\"flood_affected\\\": true}.\",\"action\":{\"tool\":\"EstimateExposure\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"exposure\":\"blocked_roads\",\"metrics\":{\"flood_affected\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"estimated exposure `exposure_1`\"\n- latest_handle = \"exposure_1\"\n- latest_tool = \"EstimateExposure\"\n- latest_tool_ok = true\n- exposure_1_evidence = {\"blocked_edges\": 4, \"edge_count\": 1294, \"max_risk\": 1.0, \"mean_risk\": 0.0071, \"risk_buffer_m\": 120, \"risky_edges\": 12, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"exposure\": \"blocked_roads\", \"metrics\": {\"flood_affected\": true}, \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"exposure_1_answer_ref\": \"ANSWER_JSON:exposure_1\", \"exposure_1_evidence\": {\"blocked_edges\": 4, \"edge_count\": 1294, \"max_risk\": 1.0, \"mean_risk\": 0.0071, \"risk_buffer_m\": 120, \"risky_edges\": 12, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload\": {\"blocked_edges\": 4, \"edge_count\": 1294, \"max_risk\": 1.0, \"mean_risk\": 0.0071, \"risk_buffer_m\": 120, \"risky_edges\": 12, \"road_buffer_m\": 8, \"roads_with_flood_risk_path\": \"source_assets/routing/000188/derived/roads_with_flood_risk.geojson\", \"status\": \"loaded_cached_flood_risk_graph\"}, \"exposure_1_payload_keys\": [\"roads_with_flood_risk_path\", \"edge_count\", \"blocked_edges\", \"risky_edges\", \"mean_risk\", \"max_risk\", \"road_buffer_m\", \"risk_buffer_m\", \"status\"], \"exposure_1_summary\": \"estimated exposure `exposure_1`\", \"latest_answer_ref\": \"ANSW...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `nearest_reachable_shelter`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"exposure_1\",\"objective\":\"nearest_reachable_shelter\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied flood-screened route candidates against the declared road-edge costs, then report the reachable shelter attached to the minimum fastest-objective candidate. Report shelter name, shelter confidence, route ID, distance, ETA, length-weighted risk score, composite objective cost, and blocked-edge count. This task validates and selects a packaged candidate; it does not run a new graph search.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / nearest shelter task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / nearest shelter: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / nearest shelter: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- exposure_1_evidence = {\"blocked_edges\": 4, \"edge_count\": 1294, \"max_risk\": 1.0, \"mean_risk\": 0.0071, \"risk_buffer_m\": 120, \"risky_edges\": 12, \"road_buffer_m\": 8, \"status\": \"loaded_cached_flood_risk_graph\"}\n- exposure_1_summary = \"estimated exposure `exposure_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e01a1b52bffc]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"nearest_reachable_shelter\", \"question\": \"question_1\", \"work\": \"exposure_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Merendero Fontana...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2383, "id": "routing/000188/nearest_shelter", "dataset_id": "routing/000188/nearest_shelter", "sample_id": "000188", "question_type": "nearest_shelter", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "EstimateExposure", "PlanRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e01a1b52bffc97e4202bde6015309ae8ed91d9ed6378cc5414a8cbfb8c10fc11"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_1 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_1\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_1\",\"role\":\"roads_shelters_flood\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_1\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `candidate_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"candidate_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1a0f343c6f67]\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"candidate_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Merendero Fontanar\"}}}, \"route...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate the supplied packaged route candidates against the declared road-edge costs. Report the minimum fastest, safest-weighted, and balanced composite-cost candidates and explain their travel-time and length-weighted flood-risk trade-off. The safest-weighted objective is not the same as minimizing mean risk alone. Do not run a new graph search or add an evacuation recommendation.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / fastest safest task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_1\"], \"objective\": \"flood-aware routing / fastest safest: prepare reusable evidence from route_context_1. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / fastest safest: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_1 [route_context] - route_context_1: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_di...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1a0f343c6f67]\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n\nTURN\n11\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2384, "id": "routing/000188/fastest_safest", "dataset_id": "routing/000188/fastest_safest", "sample_id": "000188", "question_type": "fastest_safest", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1a0f343c6f6772102e1a06b5ab3ae2cde9b50e4d9371a3ffb161c2b1abc5f1b0"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- route_context_2 [route_context] - Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.; refs: rgb_preview.png, rgb.tif, metadata.json, flood_extent.geojson\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\",\"needs\":[\"question_1\",\"route_context_2\"]},{\"id\":\"s2\",\"objective\":\"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\",\"needs\":[\"question_1\",\"work_1\"]},{\"id\":\"s3\",\"objective\":\"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"route_compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `route_context_2` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"route_context_2\",\"role\":\"roads_shelters_flood_rgb\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"route_context_2\", \"question\": \"question_1\", \"role\": \"roads_shelters_flood_rgb\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"routing\\\": \\\"scene_1\\\"} into a `flood_safe_routing` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"routing\":\"scene_1\"},\"mode\":\"flood_safe_routing\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"routing\": \"scene_1\"}, \"mode\": \"flood_safe_routing\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Route and hazard context is ready; plan for objective `overlay_routes`.\",\"action\":{\"tool\":\"PlanRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"objective\":\"overlay_routes\",\"avoid\":{\"flood_affected_roads\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"planned routes `routes_1`\"\n- latest_handle = \"routes_1\"\n- latest_tool = \"PlanRoutes\"\n- latest_tool_ok = true\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"avoid\": {\"flood_affected_roads\": true}, \"objective\": \"overlay_routes\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:routes_1\", \"latest_handle\": \"routes_1\", \"latest_summary\": \"planned routes `routes_1`\", \"latest_tool\": \"PlanRoutes\", \"latest_tool_ok\": true, \"routes_1_answer_ref\": \"ANSWER_JSON:routes_1\", \"routes_1_evidence\": {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_type\": \"fastest\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"safest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"S1\", \"route_type\": \"safest\", \"selected_shelter_name\": \"Merendero Fontanar\"}}}, \"routes_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Candidate routes are ready; compare with preference {\\\"fastest\\\": true, \\\"safest\\\": true, \\\"balanced\\\": true}.\",\"action\":{\"tool\":\"CompareRoutes\",\"args\":{\"question\":\"question_1\",\"work\":\"routes_1\",\"prefer\":{\"fastest\":true,\"safest\":true,\"balanced\":true}}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"compared routes `route_compare_1`\"\n- latest_handle = \"route_compare_1\"\n- latest_tool = \"CompareRoutes\"\n- latest_tool_ok = true\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_di...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"prefer\": {\"balanced\": true, \"fastest\": true, \"safest\": true}, \"question\": \"question_1\", \"work\": \"routes_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"latest_handle\": \"route_compare_1\", \"latest_summary\": \"compared routes `route_compare_1`\", \"latest_tool\": \"CompareRoutes\", \"latest_tool_ok\": true, \"route_compare_1_answer_ref\": \"ANSWER_JSON:route_compare_1\", \"route_compare_1_evidence\": {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_distance_m\": 0.0, \"safest_extra_eta_min\": 0.0, \"safest_risk_reduction\": 0.0}, \"fastest_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_type\": \"fastest\",...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `route_overlay` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"route_compare_1\",\"layers\":{\"rgb\":true,\"roads\":true,\"flood\":true,\"blocked_roads\":true,\"origin\":true,\"shelter\":true,\"routes\":true,\"labels\":true},\"style\":\"route_overlay\"}}}"}, {"role": "user", "content": "QUESTION\nValidate and compare the supplied packaged route candidates, then render a fresh run-scoped overlay from the declared Sentinel-2 RGB GeoTIFF, OSM-derived roads, Copernicus flood extent, flood-risk and blocked-road attributes, origin, selected shelter, and all fastest/safest-weighted/balanced route labels. Bind the image to the current comparison handle and do not retrieve a prepared overlay.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the flood-aware routing / visual overlay task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"route_context_2\"], \"objective\": \"flood-aware routing / visual overlay: prepare reusable evidence from route_context_2. Preserve units, temporal/source metadata, masks, and schema cues so later reasoning has a compact grounded work context.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"work_1\"], \"objective\": \"flood-aware routing / visual overlay: make the requested routing decision from the prepared route/flood context. Compare reachability, time, safety, risk, and blocked-edge support as needed.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"route_compare_1\"], \"objective\": \"flood-aware routing / visual overlay: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- route_context_2 [route_context] - route_context_2: Sentinel-2 context plus declared OSM-derived flood-risk roads, Copernicus flood extent, packaged route geometries, and route scores with edge-derived composite objective costs.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2385_21869cbeb486__337409fef1/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\":...\n- image_1_summary = \"rendered map `image_1`\"\n- route_compare_1_evidence = {\"balanced_route\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"delta\": {\"balanced_extra_distance_m\": 0.0, \"balanced_extra_eta_min\": 0.0, \"balanced_risk_reduction\": 0.0, \"safest_extra_di...\n- route_compare_1_summary = \"compared routes `route_compare_1`\"\n- routes_1_evidence = {\"best\": {\"balanced\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"B1\", \"route_type\": \"balanced\", \"selected_shelter_name\": \"Merendero Fontanar\"}, \"fastest\": {\"blocked_edges\": 0, \"distance_km\": 6.012, \"eta_min\": 80.15, \"risk_score\": 0.0142, \"route_id\": \"F1\", \"route_t...\n- routes_1_summary = \"planned routes `routes_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=275056fcb1af]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"question\": \"question_1\", \"style\": \"route_overlay\", \"work\": \"route_compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2385_21869cbeb486__337409fef1/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"roads\": true, \"routes\": true, \"shelter\": true}, \"style\": \"route_overlay\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2385_21869cbeb486__337409fef1/routing_live_overlay.png\", \"layers\": {\"blocked_roads\": true, \"flood\": true, \"labels\": true, \"origin\": true, \"rgb\": true, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/final_routing_subset_v1", "export_source": "geodisaster_test", "idx": 2385, "id": "routing/000188/visual_overlay", "dataset_id": "routing/000188/visual_overlay", "sample_id": "000188", "question_type": "visual_overlay", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "PlanRoutes", "CompareRoutes", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "275056fcb1af5ddae87f9d64ad59992e87161a6752c9be084fc4735780e14100"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3c85441d9d71]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_components\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3c85441d9d71]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_295782, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.18...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_components\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=3c85441d9d71]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2467, "id": "sen1_flood/Somalia_295782/A", "dataset_id": "sen1_flood/Somalia_295782/A", "sample_id": "Somalia_295782", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "3c85441d9d710f797cf7dee0f3c850185534aaea170633d70cfcd734d21494f5"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c9ea2aee3b9]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c9ea2aee3b9]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.1005...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c9ea2aee3b9]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 54.827881, \"flood_pixels\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_295782, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9c9ea2aee3b9]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 54.827881, \"flood_pixels\": 8983, \"fraction_of_total_flood\": 0.098094, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2468, "id": "sen1_flood/Somalia_295782/B", "dataset_id": "sen1_flood/Somalia_295782/B", "sample_id": "Somalia_295782", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9c9ea2aee3b934342076157a95e9051ede8f29e90a06e79aec3ab25e50033489"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a586fd94c610]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"concentration_score\": 0.106962, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a586fd94c610]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"concentration_score\": 0.106962, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 54.827881, \"flood_pixels\": 8983, \"fraction_of_total_flood\": 0.098094, \"row\": 3, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"Somalia_295782\", \"concentration_score\": 0.106962, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"f...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_295782, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"concentration_score\": 0.106962, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a586fd94c610]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 54.827881, \"flood_pixels\": 8983, \"fraction_of_total_flood\": 0.098094, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2469, "id": "sen1_flood/Somalia_295782/C", "dataset_id": "sen1_flood/Somalia_295782/C", "sample_id": "Somalia_295782", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a586fd94c610104549e3eee2c22a0fd6bc0fc3b82d9499117dcf2a68b4d70c69"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1b15f20263d9]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_295782, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=1b15f20263d9]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 100.0, \"overlap_pct_of_flood\": 0.0}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_perce...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2470, "id": "sen1_flood/Somalia_295782/D", "dataset_id": "sen1_flood/Somalia_295782/D", "sample_id": "Somalia_295782", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "1b15f20263d98f75c71c1c1dfe4fa60a79f9cee27f4f7edc5d7b01b8971f4134"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.1005...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.18...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fra...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.18...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205, \"fraction_of_total_flood\": 0.100519, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 54.827881, \"flood_pixels\": 8983, \"fraction_of_total_flood\": 0.098094, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.18...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ae1837d5dd6]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2634, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.78393...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_295782. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2471_81def7e8480f__db9a5964f0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 91575, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 59.783936, \"flood_pixels\": 9795, \"fraction_of_total_flood\": 0.106962, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 56.182861, \"flood_pixels\": 9205...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"computed_flood_pixel_count\": 91575, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.18...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2634, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"largest_component_pixel_count\": 52025, \"object_count\": 2634, \"polygon_count\": 2634, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_295782\", \"component_count\": 2634, \"computed_flood_area_km2\": 9.086077, \"computed_flood_area_m2\": 9086077.247, \"computed_flood_percentage\": 34.93309, \"concentration_score\": 0.106962, \"largest_component_area_m2\": 5161911.188, \"largest_component_centroid\": [45.642337, 2.742659], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ae1837d5dd6]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2471_81def7e8480f__db9a5964f0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2471_81def7e8480f__db9a5964f0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2471_81def7e8480f__db9a5964f0/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2471, "id": "sen1_flood/Somalia_295782/E", "dataset_id": "sen1_flood/Somalia_295782/E", "sample_id": "Somalia_295782", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "6ae1837d5dd651870ad82fdca1a61ab81dbfa1b6bb2650b63708be88a6c55157"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a84d4a363058]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_components\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a84d4a363058]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Somalia_371421, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 393338...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_components\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a84d4a363058]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2482, "id": "sen1_flood/Somalia_371421/A", "dataset_id": "sen1_flood/Somalia_371421/A", "sample_id": "Somalia_371421", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a84d4a363058d09159e250d879a4b029b75a5a5f00fca3ed4f384fddac7c5371"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be1196b652ba]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be1196b652ba]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be1196b652ba]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 56.158447, \"flood_pixels...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Somalia_371421, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=be1196b652ba]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 56.158447, \"flood_pixels\": 9201, \"fraction_of_total_flood\": 0.088179, \"row\": 4, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2483, "id": "sen1_flood/Somalia_371421/B", "dataset_id": "sen1_flood/Somalia_371421/B", "sample_id": "Somalia_371421", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "be1196b652baceb03162b7bcf8b3e65d65f62cea08e384863f2b5abc22cb553e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=53b869f205b2]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"concentration_score\": 0.093095, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=53b869f205b2]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"concentration_score\": 0.093095, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 56.158447, \"flood_pixels\": 9201, \"fraction_of_total_flood\": 0.088179, \"row\": 4, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"Somalia_371421\", \"concentration_score\": 0.093095, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"f...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip Somalia_371421, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"concentration_score\": 0.093095, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=53b869f205b2]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 56.158447, \"flood_pixels\": 9201, \"fraction_of_total_flood\": 0.088179, \"row\": 4, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2484, "id": "sen1_flood/Somalia_371421/C", "dataset_id": "sen1_flood/Somalia_371421/C", "sample_id": "Somalia_371421", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "53b869f205b26a682c446ddbec64f95e2fb81ea2f2f7a49aaecd74b9964105ba"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fdea3bc0b2f6]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"f...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Somalia_371421, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroi...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=fdea3bc0b2f6]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 100.0, \"overlap_pct_of_flood\": 0.0}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2485, "id": "sen1_flood/Somalia_371421/D", "dataset_id": "sen1_flood/Somalia_371421/D", "sample_id": "Somalia_371421", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "fdea3bc0b2f67134d922e38a01d7a665a6c52e220d0c071f5905112838fd63c0"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 393338...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 393338...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446, \"fraction_of_total_flood\": 0.090527, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 56.158447, \"flood_pixels\": 9201, \"fraction_of_total_flood\": 0.088179, \"row\": 4, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 393338...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=461db04104df]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2199, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59....\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Somalia_371421. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2486_303930cb4dab__9339337081/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 104345, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 59.289551, \"flood_pixels\": 9714, \"fraction_of_total_flood\": 0.093095, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 57.653809, \"flood_pixels\": 9446...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"computed_flood_pixel_count\": 104345, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 393338...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2199, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"largest_component_pixel_count\": 39732, \"object_count\": 2199, \"polygon_count\": 2199, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Somalia_371421\", \"component_count\": 2199, \"computed_flood_area_km2\": 10.329847, \"computed_flood_area_m2\": 10329846.879, \"computed_flood_percentage\": 39.804459, \"concentration_score\": 0.093095, \"largest_component_area_m2\": 3933382.908, \"largest_component_centroid\": [45.152981, 4.75458], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=461db04104df]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2486_303930cb4dab__9339337081/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2486_303930cb4dab__9339337081/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2486_303930cb4dab__9339337081/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2486, "id": "sen1_flood/Somalia_371421/E", "dataset_id": "sen1_flood/Somalia_371421/E", "sample_id": "Somalia_371421", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "461db04104df3ed679554381c9bd6509428753b4edb262fca042a818133b5dc9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a8924202ca4b]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percent...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_components\", \"top_3_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a8924202ca4b]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.02830...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for Bolivia_23014, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"la...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_components\", \"top_3_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a8924202ca4b]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"v...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2512, "id": "sen1_flood/Bolivia_23014/A", "dataset_id": "sen1_flood/Bolivia_23014/A", "sample_id": "Bolivia_23014", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a8924202ca4b9e836d7125c1f9d35b9bd0840cd1313a18ed9aa0d3197b90ea2d"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ba932c1c1fd]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percent...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ba932c1c1fd]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ba932c1c1fd]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 6.962153, \"flood_pixels\": 34...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip Bolivia_23014, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=6ba932c1c1fd]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 6.962153, \"flood_pixels\": 344, \"fraction_of_total_flood\": 0.040065, \"row\": 1, \"valid_pixels\": 4941}]}, \"rank_1_payload\": {\"all_candidates\": [{\"c...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2513, "id": "sen1_flood/Bolivia_23014/B", "dataset_id": "sen1_flood/Bolivia_23014/B", "sample_id": "Bolivia_23014", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "6ba932c1c1fd8c1826a2b8fda9d997951d8ef577aec4ad32fcd98321a65dfc28"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e6f609649596]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percent...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"concentration_score\": 0.673538, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e6f609649596]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"concentration_score\": 0.673538, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 6.962153, \"flood_pixels\": 344, \"fraction_of_total_flood\": 0.040065, \"row\": 1, \"valid_pixels\": 4941}]}, \"flood_report_1_payload\": {\"chip_id\": \"Bolivia_23014\", \"concentration_score\": 0.673538, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip Bolivia_23014, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"concentration_score\": 0.673538, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e6f609649596]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 6.962153, \"flood_pixels\": 344, \"fraction_of_total_flood\": 0.040065, \"row\": 1, \"valid_pixels\": 4941}]}, \"rank_1_payload\": {\"all_candidates\": [{\"c...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2514, "id": "sen1_flood/Bolivia_23014/C", "dataset_id": "sen1_flood/Bolivia_23014/C", "sample_id": "Bolivia_23014", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e6f6096495964dc5eb40963dc7b370e9638cf025c5daa72b5f06581d14d851ea"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percent...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=55d13665808a]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"floo...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for Bolivia_23014, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.8...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=55d13665808a]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 20.859539, \"overlap_pct_of_flood\": 79.140461}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_p...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2515, "id": "sen1_flood/Bolivia_23014/D", "dataset_id": "sen1_flood/Bolivia_23014/D", "sample_id": "Bolivia_23014", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "55d13665808ad6dedb051de90c64698ac344b0403abc68a659d8b7bd9588a219"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percent...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"la...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"la...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243, \"fraction_of_total_flood\": 0.028302, \"row\": 2, \"valid_pixels\": 1218}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 6.962153, \"flood_pixels\": 344, \"fraction_of_total_flood\": 0.040065, \"row\": 1, \"valid_pixels\": 4941}]}, \"rank_1_payload\": {\"all_candidates\": [{\"c...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"la...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=140ce1c1a2b4]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 244, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip Bolivia_23014. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2516_2e7ed91d20ad__c3585c6921/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839}, \"outside_reference_pct_of_flood\": 20.859539, \"outside_reference_pixels\": 1791, \"overlap_pct_of_flood\": 79.140461, \"overlap_pixels\": 6795}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C1\", \"col\": 1, \"flood_percentage\": 35.296631, \"flood_pixels\": 5783, \"fraction_of_total_flood\": 0.673538, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C4\", \"col\": 4, \"flood_percentage\": 19.950739, \"flood_pixels\": 243,...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"computed_flood_pixel_count\": 8586, \"computed_valid_pixel_count\": 193558, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"la...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 244, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"largest_component_pixel_count\": 5841, \"object_count\": 244, \"polygon_count\": 244, \"target\": \"flood_polygons\", \"top_3_gr...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"Bolivia_23014\", \"component_count\": 244, \"computed_flood_area_km2\": 0.829544, \"computed_flood_area_m2\": 829543.545, \"computed_flood_percentage\": 4.43588, \"concentration_score\": 0.673538, \"largest_component_area_m2\": 564315.839, \"largest_component_centroid\": [-64.894325, -13.610213], \"mask_source\": \"gener...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=140ce1c1a2b4]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2516_2e7ed91d20ad__c3585c6921/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2516_2e7ed91d20ad__c3585c6921/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2516_2e7ed91d20ad__c3585c6921/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2516, "id": "sen1_flood/Bolivia_23014/E", "dataset_id": "sen1_flood/Bolivia_23014/E", "sample_id": "Bolivia_23014", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "140ce1c1a2b486752656ba145da6fec6825fcf6091401cd536178fe687643352"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9d8f62ca951b]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_components\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9d8f62ca951b]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1017769, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_components\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=9d8f62ca951b]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"ro...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2572, "id": "sen1_flood/India_1017769/A", "dataset_id": "sen1_flood/India_1017769/A", "sample_id": "India_1017769", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "9d8f62ca951bd0568d324f43fcf19f07cdd959163126b881bfd7449b1bacf66b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=35c7b18128c1]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=35c7b18128c1]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.1...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=35c7b18128c1]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1017769\", \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_percentage\": 91.07666, \"flood_pixel...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1017769, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 154...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=35c7b18128c1]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_percentage\": 91.07666, \"flood_pixels\": 14922, \"fraction_of_total_flood\": 0.101704, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2573, "id": "sen1_flood/India_1017769/B", "dataset_id": "sen1_flood/India_1017769/B", "sample_id": "India_1017769", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "35c7b18128c124be2818135d6fa40ff8509850d21f485b755aaafc3476d5c0d9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2802257f966]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"concentration_score\": 0.106373, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2802257f966]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1017769\", \"concentration_score\": 0.106373, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_percentage\": 91.07666, \"flood_pixels\": 14922, \"fraction_of_total_flood\": 0.101704, \"row\": 1, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_1017769\", \"concentration_score\": 0.106373, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"f...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1017769, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 154...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"concentration_score\": 0.106373, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2802257f966]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_percentage\": 91.07666, \"flood_pixels\": 14922, \"fraction_of_total_flood\": 0.101704, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2574, "id": "sen1_flood/India_1017769/C", "dataset_id": "sen1_flood/India_1017769/C", "sample_id": "India_1017769", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c2802257f966cfa500806515b8ae65f28605d0a2e96a3720630532d844b38387"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c4c41846b8cd]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"c...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1017769, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c4c41846b8cd]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 87.654716, \"overlap_pct_of_flood\": 12.345284}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2575, "id": "sen1_flood/India_1017769/D", "dataset_id": "sen1_flood/India_1017769/D", "sample_id": "India_1017769", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c4c41846b8cd83780a93a67c321b1cbacb5624f246060c962765d3e8c1423a11"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.1...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452,...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 154...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 15452, \"fraction_of_total_flood\": 0.105316, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_percentage\": 91.07666, \"flood_pixels\": 14922, \"fraction_of_total_flood\": 0.101704, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 154...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2c4782ab0ba]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 428, \"top_3_grid_cells\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percen...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1017769. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2576_7df3f936b97a__48b1378699/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704}, \"outside_reference_pct_of_flood\": 87.654716, \"outside_reference_pixels\": 128607, \"overlap_pct_of_flood\": 12.345284, \"overlap_pixels\": 18113}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 95.257568, \"flood_pixels\": 15607, \"fraction_of_total_flood\": 0.106373, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 94.311523, \"flood_pixels\": 154...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"computed_flood_pixel_count\": 146720, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 428, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"largest_component_pixel_count\": 143513, \"object_count\": 428, \"polygon_count\": 428, \"target\": \"flood_polygons\", \"top...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1017769\", \"component_count\": 428, \"computed_flood_area_km2\": 13.064935, \"computed_flood_area_m2\": 13064935.01, \"computed_flood_percentage\": 55.969238, \"concentration_score\": 0.106373, \"largest_component_area_m2\": 12779328.704, \"largest_component_centroid\": [93.021632, 26.612133], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c2c4782ab0ba]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2576_7df3f936b97a__48b1378699/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2576_7df3f936b97a__48b1378699/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2576_7df3f936b97a__48b1378699/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2576, "id": "sen1_flood/India_1017769/E", "dataset_id": "sen1_flood/India_1017769/E", "sample_id": "India_1017769", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c2c4782ab0ba54470df7e915da037a71d1cd0b4b6c6e0357eaa149063ab06871"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=42ac06ef1f3e]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_p...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=42ac06ef1f3e]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_1050276, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.91...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=42ac06ef1f3e]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2592, "id": "sen1_flood/India_1050276/A", "dataset_id": "sen1_flood/India_1050276/A", "sample_id": "India_1050276", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "42ac06ef1f3efac38a3963debb206f8aca5c0f558936756d6123a19c735737fd"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2f2c51ca4ae0]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_p...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2f2c51ca4ae0]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.09...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2f2c51ca4ae0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1050276\", \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_percentage\": 43.548584, \"flood_pixels\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_1050276, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2f2c51ca4ae0]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_percentage\": 43.548584, \"flood_pixels\": 7135, \"fraction_of_total_flood\": 0.090983, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2593, "id": "sen1_flood/India_1050276/B", "dataset_id": "sen1_flood/India_1050276/B", "sample_id": "India_1050276", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2f2c51ca4ae0430a64e7ed75d95c52e6a4672bdb54eadc821796109faec539ef"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=509c8f159062]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_p...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"concentration_score\": 0.106668, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=509c8f159062]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1050276\", \"concentration_score\": 0.106668, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_percentage\": 43.548584, \"flood_pixels\": 7135, \"fraction_of_total_flood\": 0.090983, \"row\": 2, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_1050276\", \"concentration_score\": 0.106668, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"flo...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_1050276, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"concentration_score\": 0.106668, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=509c8f159062]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_percentage\": 43.548584, \"flood_pixels\": 7135, \"fraction_of_total_flood\": 0.090983, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2594, "id": "sen1_flood/India_1050276/C", "dataset_id": "sen1_flood/India_1050276/C", "sample_id": "India_1050276", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "509c8f1590626e56a64a4a1314c525ef4896621812cdd6031e3bf8c488012d2c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_p...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=785db3147f5c]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_1050276, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=785db3147f5c]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 94.099795, \"overlap_pct_of_flood\": 5.900205}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_fl...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2595, "id": "sen1_flood/India_1050276/D", "dataset_id": "sen1_flood/India_1050276/D", "sample_id": "India_1050276", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "785db3147f5c35350d2cc57c91bc9b5fb1efb6096a87b660b75f10a1f2beacaf"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_p...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.09...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.91...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fr...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.91...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455, \"fraction_of_total_flood\": 0.095064, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C1\", \"col\": 1, \"flood_percentage\": 43.548584, \"flood_pixels\": 7135, \"fraction_of_total_flood\": 0.090983, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.91...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5cd2fcc599c]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2399, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percenta...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_1050276. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2596_1e67d5333a92__67119748d0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916}, \"outside_reference_pct_of_flood\": 94.099795, \"outside_reference_pixels\": 73794, \"overlap_pct_of_flood\": 5.900205, \"overlap_pixels\": 4627}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C4\", \"col\": 4, \"flood_percentage\": 51.055908, \"flood_pixels\": 8365, \"fraction_of_total_flood\": 0.106668, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 45.501709, \"flood_pixels\": 7455...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"computed_flood_pixel_count\": 78421, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.91...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2399, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"largest_component_pixel_count\": 13650, \"object_count\": 2399, \"polygon_count\": 2399, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_1050276\", \"component_count\": 2399, \"computed_flood_area_km2\": 7.007953, \"computed_flood_area_m2\": 7007952.889, \"computed_flood_percentage\": 29.915237, \"concentration_score\": 0.106668, \"largest_component_area_m2\": 1219913.916, \"largest_component_centroid\": [92.532463, 26.183018], \"mask_source\": \"ge...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=a5cd2fcc599c]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2596_1e67d5333a92__67119748d0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2596_1e67d5333a92__67119748d0/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2596_1e67d5333a92__67119748d0/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2596, "id": "sen1_flood/India_1050276/E", "dataset_id": "sen1_flood/India_1050276/E", "sample_id": "India_1050276", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "a5cd2fcc599cc3d66c87b83ea5af38df88e0ec6bececac37f9da0ebda42db209"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=80d3d47c9ae2]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=80d3d47c9ae2]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_664410, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=80d3d47c9ae2]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2777, "id": "sen1_flood/India_664410/A", "dataset_id": "sen1_flood/India_664410/A", "sample_id": "India_664410", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "80d3d47c9ae2819f8bd1838e397db3ea18ad5e133194f402195c4f10cfdda24e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f6aabbe7b5a1]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f6aabbe7b5a1]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f6aabbe7b5a1]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_664410\", \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 54.052734, \"flood_pixels\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_664410, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 935...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=f6aabbe7b5a1]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 54.052734, \"flood_pixels\": 8856, \"fraction_of_total_flood\": 0.081927, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2778, "id": "sen1_flood/India_664410/B", "dataset_id": "sen1_flood/India_664410/B", "sample_id": "India_664410", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "f6aabbe7b5a1258aa2a3619145d3adf36f64afdf5fa0f8f54c10dbbacd0ba9e2"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8e1f1e32ebe1]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"concentration_score\": 0.095804, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8e1f1e32ebe1]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_664410\", \"concentration_score\": 0.095804, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 54.052734, \"flood_pixels\": 8856, \"fraction_of_total_flood\": 0.081927, \"row\": 1, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_664410\", \"concentration_score\": 0.095804, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"floo...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_664410, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 935...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"concentration_score\": 0.095804, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8e1f1e32ebe1]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 54.052734, \"flood_pixels\": 8856, \"fraction_of_total_flood\": 0.081927, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2779, "id": "sen1_flood/India_664410/C", "dataset_id": "sen1_flood/India_664410/C", "sample_id": "India_664410", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8e1f1e32ebe11d04b61d02ab8ee70af658085e27b7e545ea348743e576a12075"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5027d71e2be0]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_664410, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=5027d71e2be0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 99.949119, \"overlap_pct_of_flood\": 0.050881}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2780, "id": "sen1_flood/India_664410/D", "dataset_id": "sen1_flood/India_664410/D", "sample_id": "India_664410", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "5027d71e2be055c60a459a27d85f8396878b86cb9430ba217f32bb4bcd5079fa"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.0...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fr...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 935...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 9350, \"fraction_of_total_flood\": 0.086497, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C4\", \"col\": 4, \"flood_percentage\": 54.052734, \"flood_pixels\": 8856, \"fraction_of_total_flood\": 0.081927, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 935...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0a06b105b2c7]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2081, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_664410. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2781_86da71f83f9b__d6c6e7c538/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508}, \"outside_reference_pct_of_flood\": 99.949119, \"outside_reference_pixels\": 108041, \"overlap_pct_of_flood\": 0.050881, \"overlap_pixels\": 55}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C4\", \"col\": 4, \"flood_percentage\": 63.208008, \"flood_pixels\": 10356, \"fraction_of_total_flood\": 0.095804, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 57.067871, \"flood_pixels\": 935...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"computed_flood_pixel_count\": 108096, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2081, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"largest_component_pixel_count\": 26298, \"object_count\": 2081, \"polygon_count\": 2081, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_664410\", \"component_count\": 2081, \"computed_flood_area_km2\": 9.67106, \"computed_flood_area_m2\": 9671059.682, \"computed_flood_percentage\": 41.235352, \"concentration_score\": 0.095804, \"largest_component_area_m2\": 2353041.508, \"largest_component_centroid\": [93.122944, 26.042629], \"mask_source\": \"gene...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0a06b105b2c7]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2781_86da71f83f9b__d6c6e7c538/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2781_86da71f83f9b__d6c6e7c538/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2781_86da71f83f9b__d6c6e7c538/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2781, "id": "sen1_flood/India_664410/E", "dataset_id": "sen1_flood/India_664410/E", "sample_id": "India_664410", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0a06b105b2c79543c2074ffdccad103f27d46c09d306469a9081fd67124b05f3"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=736fc8a81283]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_perce...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_components\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=736fc8a81283]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_70352, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_components\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=736fc8a81283]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2792, "id": "sen1_flood/India_70352/A", "dataset_id": "sen1_flood/India_70352/A", "sample_id": "India_70352", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "736fc8a81283889451aef8edccf2589efb81146a7c802f11c0e58b8c33714015"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b2f07c04428]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_perce...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b2f07c04428]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.1318...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b2f07c04428]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_70352\", \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 18.371582, \"flood_pixels\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_70352, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=8b2f07c04428]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 18.371582, \"flood_pixels\": 3010, \"fraction_of_total_flood\": 0.130847, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2793, "id": "sen1_flood/India_70352/B", "dataset_id": "sen1_flood/India_70352/B", "sample_id": "India_70352", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "8b2f07c04428673af4486fb7ddbaeca9745c66be4d75f82352fc18e6663ab4c0"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d38040e57afd]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_perce...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"concentration_score\": 0.184446, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_per...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d38040e57afd]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_70352\", \"concentration_score\": 0.184446, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 18.371582, \"flood_pixels\": 3010, \"fraction_of_total_flood\": 0.130847, \"row\": 3, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_70352\", \"concentration_score\": 0.184446, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_p...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_70352, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"concentration_score\": 0.184446, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_per...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d38040e57afd]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 18.371582, \"flood_pixels\": 3010, \"fraction_of_total_flood\": 0.130847, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2794, "id": "sen1_flood/India_70352/C", "dataset_id": "sen1_flood/India_70352/C", "sample_id": "India_70352", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d38040e57afd46fb3668c8ebcb6d8cecf58dcf41f244265cffdd5fb19828820b"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_perce...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=34534ae59834]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1,...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_70352, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=34534ae59834]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 62.158755, \"overlap_pct_of_flood\": 37.841245}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_p...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2795, "id": "sen1_flood/India_70352/D", "dataset_id": "sen1_flood/India_70352/D", "sample_id": "India_70352", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "34534ae598341aab7fad3ae799f095fd008dbc6480784566c2153b15b717392c"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_perce...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.1318...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fracti...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033, \"fraction_of_total_flood\": 0.131847, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 18.371582, \"flood_pixels\": 3010, \"fraction_of_total_flood\": 0.130847, \"row\": 3, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2335fdbb4f30]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1416, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\":...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_70352. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2796_401c137bc907__bd46a44964/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574}, \"outside_reference_pct_of_flood\": 62.158755, \"outside_reference_pixels\": 14299, \"overlap_pct_of_flood\": 37.841245, \"overlap_pixels\": 8705}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 25.897217, \"flood_pixels\": 4243, \"fraction_of_total_flood\": 0.184446, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R4_C3\", \"col\": 3, \"flood_percentage\": 18.511963, \"flood_pixels\": 3033...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"computed_flood_pixel_count\": 23004, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1416, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"largest_component_pixel_count\": 13543, \"object_count\": 1416, \"polygon_count\": 1416, \"target\": \"flood_polygons\", \"top_...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_70352\", \"component_count\": 1416, \"computed_flood_area_km2\": 2.061268, \"computed_flood_area_m2\": 2061267.955, \"computed_flood_percentage\": 8.77533, \"concentration_score\": 0.184446, \"largest_component_area_m2\": 1213550.574, \"largest_component_centroid\": [92.879112, 25.866391], \"mask_source\": \"genera...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=2335fdbb4f30]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2796_401c137bc907__bd46a44964/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2796_401c137bc907__bd46a44964/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2796_401c137bc907__bd46a44964/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2796, "id": "sen1_flood/India_70352/E", "dataset_id": "sen1_flood/India_70352/E", "sample_id": "India_70352", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "2335fdbb4f30ec5d5175fec14c9ee62f5fb54b42ddf4a787e598980edb5d6619"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c90763170211]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_components\",...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c90763170211]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_764946, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_components\",...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=c90763170211]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2817, "id": "sen1_flood/India_764946/A", "dataset_id": "sen1_flood/India_764946/A", "sample_id": "India_764946", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "c90763170211626b9fbdf53db6b5cc39ff93c79e7ebc8e8dae82162389353910"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b0d55137b729]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b0d55137b729]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.0...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b0d55137b729]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_764946\", \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 47.10083, \"flood_pixels\"...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_764946, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=b0d55137b729]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 47.10083, \"flood_pixels\": 7717, \"fraction_of_total_flood\": 0.069026, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2818, "id": "sen1_flood/India_764946/B", "dataset_id": "sen1_flood/India_764946/B", "sample_id": "India_764946", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "b0d55137b72991ebeb0eda43e959e0f5254e9c5af2bb664b9922ca19de711966"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c7e0e72d1c0]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"concentration_score\": 0.069474, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_pe...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c7e0e72d1c0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_764946\", \"concentration_score\": 0.069474, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 47.10083, \"flood_pixels\": 7717, \"fraction_of_total_flood\": 0.069026, \"row\": 1, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_764946\", \"concentration_score\": 0.069474, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_764946, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"concentration_score\": 0.069474, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_pe...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c7e0e72d1c0]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 47.10083, \"flood_pixels\": 7717, \"fraction_of_total_flood\": 0.069026, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2819, "id": "sen1_flood/India_764946/C", "dataset_id": "sen1_flood/India_764946/C", "sample_id": "India_764946", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4c7e0e72d1c07673603162d2ce68ef36cfdaebef1bb9883a23ef63b292e6a10f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ff4ac51f878b]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"co...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_764946, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=ff4ac51f878b]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 99.623428, \"overlap_pct_of_flood\": 0.376572}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2820, "id": "sen1_flood/India_764946/D", "dataset_id": "sen1_flood/India_764946/D", "sample_id": "India_764946", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "ff4ac51f878bad2b6d593371ddd3cda510644d4851b8fedf070efe2cb78c6271"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.0...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718, \"fraction_of_total_flood\": 0.069035, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 47.10083, \"flood_pixels\": 7717, \"fraction_of_total_flood\": 0.069026, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=588607ac666e]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 1815, \"top_3_grid_cells\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percent...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_764946. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2821_086a340ffd96__d113497b8c/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508}, \"outside_reference_pct_of_flood\": 99.623428, \"outside_reference_pixels\": 111377, \"overlap_pct_of_flood\": 0.376572, \"overlap_pixels\": 421}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R2_C3\", \"col\": 3, \"flood_percentage\": 47.406006, \"flood_pixels\": 7767, \"fraction_of_total_flood\": 0.069474, \"row\": 2, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C3\", \"col\": 3, \"flood_percentage\": 47.106934, \"flood_pixels\": 7718...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"computed_flood_pixel_count\": 111798, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 1815, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"largest_component_pixel_count\": 57939, \"object_count\": 1815, \"polygon_count\": 1815, \"target\": \"flood_polygons\", \"t...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_764946\", \"component_count\": 1815, \"computed_flood_area_km2\": 10.028946, \"computed_flood_area_m2\": 10028945.528, \"computed_flood_percentage\": 42.647552, \"concentration_score\": 0.069474, \"largest_component_area_m2\": 5197365.508, \"largest_component_centroid\": [93.074112, 25.735485], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=588607ac666e]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2821_086a340ffd96__d113497b8c/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2821_086a340ffd96__d113497b8c/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2821_086a340ffd96__d113497b8c/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2821, "id": "sen1_flood/India_764946/E", "dataset_id": "sen1_flood/India_764946/E", "sample_id": "India_764946", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "588607ac666e2508471512872ddfbb22bbb8474c240aa12a1551bce7e31ae293"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=39cf8e81dcb0]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=39cf8e81dcb0]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\"...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_772630, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=39cf8e81dcb0]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"ro...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2827, "id": "sen1_flood/India_772630/A", "dataset_id": "sen1_flood/India_772630/A", "sample_id": "India_772630", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "39cf8e81dcb003d5a912e5f71fefec9238cd83a238df6f8fd809cdce8bf06f7f"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e0d4c6d1493]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e0d4c6d1493]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0....\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e0d4c6d1493]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_772630\", \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 62.60376, \"flood_pixel...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_772630, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 103...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\":...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=0e0d4c6d1493]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 62.60376, \"flood_pixels\": 10257, \"fraction_of_total_flood\": 0.07423, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2828, "id": "sen1_flood/India_772630/B", "dataset_id": "sen1_flood/India_772630/B", "sample_id": "India_772630", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "0e0d4c6d14939f67f875ead838cb1675043bc2433cb388ab012eb50a08486ac9"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=399c80a841d4]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"concentration_score\": 0.077226, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=399c80a841d4]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_772630\", \"concentration_score\": 0.077226, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 62.60376, \"flood_pixels\": 10257, \"fraction_of_total_flood\": 0.07423, \"row\": 2, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_772630\", \"concentration_score\": 0.077226, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"floo...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_772630, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 103...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"concentration_score\": 0.077226, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_p...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=399c80a841d4]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 62.60376, \"flood_pixels\": 10257, \"fraction_of_total_flood\": 0.07423, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2829, "id": "sen1_flood/India_772630/C", "dataset_id": "sen1_flood/India_772630/C", "sample_id": "India_772630", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "399c80a841d4b31d362b656395f2aff9473117ddcd9cc9cd8220259ec32e4cb1"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e8355fcef2dc]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"floo...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_772630, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\"...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=e8355fcef2dc]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 100.0, \"overlap_pct_of_flood\": 0.0}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_per...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2830, "id": "sen1_flood/India_772630/D", "dataset_id": "sen1_flood/India_772630/D", "sample_id": "India_772630", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "e8355fcef2dc63841cfead364cc8e270b927d3c008b1587934132da1b2136eb7"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0....\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344,...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 103...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 10344, \"fraction_of_total_flood\": 0.074859, \"row\": 1, \"valid_pixels\": 16384}, {\"cell_id\": \"R2_C2\", \"col\": 2, \"flood_percentage\": 62.60376, \"flood_pixels\": 10257, \"fraction_of_total_flood\": 0.07423, \"row\": 2, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\"...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 103...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d4bdebe496d5]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 449, \"top_3_grid_cells\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_772630. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2831_04847b8b1b95__e4d91dfada/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109}, \"outside_reference_pct_of_flood\": 100.0, \"outside_reference_pixels\": 138179, \"overlap_pct_of_flood\": 0.0, \"overlap_pixels\": 0}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R3_C1\", \"col\": 1, \"flood_percentage\": 65.130615, \"flood_pixels\": 10671, \"fraction_of_total_flood\": 0.077226, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C3\", \"col\": 3, \"flood_percentage\": 63.134766, \"flood_pixels\": 103...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"computed_flood_pixel_count\": 138179, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825....\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 449, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"largest_component_pixel_count\": 116087, \"object_count\": 449, \"polygon_count\": 449, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_772630\", \"component_count\": 449, \"computed_flood_area_km2\": 12.241139, \"computed_flood_area_m2\": 12241139.482, \"computed_flood_percentage\": 52.711105, \"concentration_score\": 0.077226, \"largest_component_area_m2\": 10283825.109, \"largest_component_centroid\": [93.020741, 27.208566], \"mask_source\": \"g...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=d4bdebe496d5]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2831_04847b8b1b95__e4d91dfada/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2831_04847b8b1b95__e4d91dfada/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2831_04847b8b1b95__e4d91dfada/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2831, "id": "sen1_flood/India_772630/E", "dataset_id": "sen1_flood/India_772630/E", "sample_id": "India_772630", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "d4bdebe496d5bc2bd355a4df4d981a680b3025659e08706b3782196c58359d00"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88c0a2272bd8]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_components` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_components\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88c0a2272bd8]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_components\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_components\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\":...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the Sentinel-1 SAR chip for India_773682, generate a SAR-derived flood emergency inventory. Report predicted flood percentage, exact-grid predicted flood area, number of 8-neighbour connected flood regions, largest-region area and centroid, and the full 4x4 spatial grid.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic A task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic A: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_components\", \"...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=88c0a2272bd8]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2832, "id": "sen1_flood/India_773682/A", "dataset_id": "sen1_flood/India_773682/A", "sample_id": "India_773682", "question_type": "A", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "88c0a2272bd8b47f1eb957108fee5dddcedb86531815bb15a58258bfdbe23387"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=dd518759721a]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=dd518759721a]\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.07...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"polygon_count\\\": true, \\\"total_area\\\": true, \\\"largest_polygon\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"polygon_count\":true,\"total_area\":true,\"largest_polygon\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=dd518759721a]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"grid\": \"4x4\", \"largest_polygon\": true, \"polygon_count\": true, \"total_area\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_773682\", \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 44.73877, \"flood_pixels\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nUsing SAR-derived flood extraction for chip India_773682, create a run-scoped flood-polygon GeoJSON. Report whether geometry was generated, polygon count, total predicted flood area, largest polygon area, bounding box and centroid, and the top 3 flooded 4x4 cells.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic B task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic B: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=dd518759721a]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 44.73877, \"flood_pixels\": 7330, \"fraction_of_total_flood\": 0.076556, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2833, "id": "sen1_flood/India_773682/B", "dataset_id": "sen1_flood/India_773682/B", "sample_id": "India_773682", "question_type": "B", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "dd518759721a38ba20d64e0a328f4a9e745b475ab602b1e255e3407f0a9640be"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\",\"needs\":[\"question_1\",\"flood_report_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\"} into a `sar_flood` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\"},\"mode\":\"sar_flood\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\"}, \"mode\": \"sar_flood\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=19ad5326b72a]\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"grid\\\": \\\"4x4\\\", \\\"concentration\\\": true, \\\"spatial_pattern\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"metrics\":{\"grid\":\"4x4\",\"concentration\":true,\"spatial_pattern\":true}}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"concentration_score\": 0.082436, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=19ad5326b72a]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"concentration\": true, \"grid\": \"4x4\", \"spatial_pattern\": true}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_773682\", \"concentration_score\": 0.082436, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 44.73877, \"flood_pixels\": 7330, \"fraction_of_total_flood\": 0.076556, \"row\": 1, \"valid_pixels\": 16384}]}, \"flood_report_1_payload\": {\"chip_id\": \"India_773682\", \"concentration_score\": 0.082436, \"grid_4x4\": [{\"cell_id\": \"R1_C1\", \"col\": 1, \"flood_...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nFor chip India_773682, estimate flood extent from the SAR image and analyze spatial concentration on a 4x4 grid. Report the top 3 cells by predicted flood percentage, the maximum-cell share of total predicted flood, and whether the result is localized or dispersed under the declared 0.5 threshold.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic C task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: prepare SAR flood evidence from sar_chip_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic C: rank the requested regions, cells, components, or candidates. Report the top item(s) with score/count/area support.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"concentration_score\": 0.082436, \"method\": \"s1_min_backscatter_otsu_v1\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_pe...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=19ad5326b72a]\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 44.73877, \"flood_pixels\": 7330, \"fraction_of_total_flood\": 0.076556, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n13\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2834, "id": "sen1_flood/India_773682/C", "dataset_id": "sen1_flood/India_773682/C", "sample_id": "India_773682", "question_type": "C", "final_split": "test", "tool_sequence": ["ReadData", "PrepareScene", "MapFlood", "DescribeFlood", "RankRegions"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "19ad5326b72a6b4020f3394dddd0f1343e4845ae7203fb0df5a8243de16c72ae"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_reference` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_reference\",\"checks\":{}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_reference\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"overlap_vs_outside_reference\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"overlap_vs_outside_reference\"}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=748603f851d6]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"overlap_vs_outside_reference\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\":...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"known_water_overlap\\\": true, \\\"outside_reference\\\": true}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"known_water_overlap\":true,\"outside_reference\":true}}}}"}, {"role": "user", "content": "QUESTION\nUsing the SAR chip and required JRC known-water reference for India_773682, separate predicted flood overlapping known water from predicted flood outside that reference. Report total predicted flood percentage and area, overlap and outside-reference counts and percentages, and the largest connected-region area and centroid. Do not call outside-reference pixels new flood.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic D task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"flood_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic D: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}\n- compare_1_summary = \"compared sources `compare_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=748603f851d6]\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"known_water_overlap\": true, \"outside_reference\": true, \"percentage\": true}, \"question\": \"question_1\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"outside_reference_pct_of_flood\": 99.696074, \"overlap_pct_of_flood\": 0.303926}, \"flood_report_1_payload\": {\"area_status\": \"wgs84_ellipsoidal_cell_sum_v1\", \"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_floo...\n\nTURN\n15\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2835, "id": "sen1_flood/India_773682/D", "dataset_id": "sen1_flood/India_773682/D", "sample_id": "India_773682", "question_type": "D", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "CompareSources", "DescribeFlood"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "748603f851d6a1dd5f9ba543b6107f4a62bf6470fe9d4a1fc3a3833b42b39c2e"} +{"messages": [{"role": "system", "content": "You are a single geospatial-disaster tool-use agent.\nYou do your own planning and tool execution. There are no specialist agents and\nno orchestrator. You receive one question plus visible handles, then solve it\nby emitting one JSON object per turn.\n\nResponse schema:\n{\n \"thought\": \"\",\n \"action\": \n}\n\nActions:\n- Plan = {\"tool\":\"Plan\",\"goal\":\"...\",\"steps\":[{\"id\":\"s1\",\"objective\":\"...\",\"needs\":[\"...\"]}]}\n- ToolCall = {\"tool\":\"\",\"args\":{...}}\n- FinalAnswer = {\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}\n\nCore rules:\n- On the first turn, produce a high-level Plan unless the answer is already\n visible. The plan should explain evidence objectives, not list every tool by\n rote.\n- After a Plan, execute one tool call per turn. Use LAST OBSERVATION and SHARED\n MEMORY to decide the next action.\n- Use exact tool names and exact argument names from ALLOWED TOOLS. Do not\n invent keys or handles.\n- Use visible canonical handles, not raw filesystem paths. For GeoDisaster\n questions, `question` is normally `\"question_1\"`.\n- Raw GeoDisaster handles such as `optical_stack_1`, `damage_scene_1`,\n `sar_chip_1`, `hazard_stack_1`, and `route_network_1` are opened with\n ReadData. Later GeoDisaster tools consume produced handles such as `scene_N`,\n `work_N`, `stats_N`, `compare_N`, `rank_N`, `route_N`, or `map_N`.\n- When ReadData returns `latest_handle = scene_N`, use that produced scene\n handle in later PrepareScene calls. Do not pass the raw input handle where a\n work/scene handle is required.\n- For PrepareScene, choose semantic slot names from the evidence: forest\n optical/sar/forest/loss_history; hazard hazard/sources/exposure; damage\n image/labels/masks; flood sar/water; routing routing; comparison scene_a and\n scene_b.\n- Stop when the evidence needed for the answer is present. Do not re-run a\n successful tool call with the same arguments.\n- Finish only when AVAILABLE ANSWER REFERENCES publishes\n `ANSWER_JSON:answer_1`, then copy it into `answer_ref`. The runtime resolves\n it only from a schema-validated projection of worker evidence. Inline answer\n JSON, prose, partial fields, worker handles, and invented references are\n rejected.\n- Reply with one JSON object only. No prose, no markdown fences.\n\nALLOWED TOOLS\n- AssessDamage(question: str, work: str, aggregation: 'scene'|'grid_4x4'|'quadrant_2x2'|'severe_only'|'pair_compare', severity?: 'severe'|'major_or_destroyed') # Aggregate building-damage polygons into scene, grid, quadrant, severe-only, or pair-comparison summaries.\n- ClassifyChange(question: str, work: str, labels: dict, rule: 'forest_loss'|'change_window'|'false_positive_type'|'canopy_density'|'concordance'|'cross_modal_concordance') # Classify a change, canopy state, concordance state, or false-positive category using explicit labels/rules.\n- CompareRoutes(question: str, work: str, prefer: dict) # Compare requested validated fastest, safest-weighted, and balanced candidates and compute the full trade-off when all three are requested, without adding an evacuation recommendation.\n- CompareScenes(question: str, work: str, by: 'severe_damage_ratio_and_footprint') # Compare two prepared scene summaries and decide which scene has the larger damage/risk signal.\n- CompareSources(question: str, work: str, compare: dict) # Compare multiple modalities or source layers, such as optical-vs-SAR, multi-source hazard agreement, or predicted-flood overlap versus pixels outside a known-water reference. Known-water context does not by itself establish temporal novelty.\n- ComputeStats(question: str, work: str, metrics: dict, region?: 'aoi'|'scene') # Compute numeric statistics for a work/damage/hazard handle, including area, counts, means, z-scores, class histograms, or footprint summaries.\n- DescribeFlood(question: str, work: str, metrics: dict) # Select and summarize requested flood-mask/component/polygon metrics, including exact-grid areas, centroids, 4x4 grids, and known-water overlap statistics from the causal handle.\n- DetectChange(question: str, work: str, change: 'largest_loss_year'|'dominant_clearing_interval', window?: '2001_2023'|'pre_post_epochs') # Detect or localize temporal change windows or dominant change events from a work handle.\n- EstimateExposure(question: str, work: str, exposure: 'blocked_roads'|'high_no2_column_population'|'highway_representatives_within_5km'|'nearest_highway'|'population_in_footprint'|'schools', metrics: dict) # Estimate exposure of people/assets/roads/facilities to a hazard footprint or blocked-road layer.\n- ExtractObjects(question: str, work: str, target: 'forest_loss_patches'|'severe_buildings'|'flood_polygons'|'flood_components', filters?: dict) # Extract object sets such as forest-loss patches, severe buildings, flood polygons, or flood components. Sentinel flood polygons and components use 8-neighbour connectivity and produce validated run-scoped GeoJSON or label-raster artifacts.\n- MapFlood(question: str, work: str, method: 's1_min_backscatter_otsu_v1') # Produce a run-scoped 0/1/255 candidate-flood mask from a prepared SAR scene. The published Sentinel contract supports the validated s1_min_backscatter_otsu_v1 method.\n- MeasureArea(question: str, work: str, target: 'forest_loss', unit: 'hectares') # Measure area for extracted objects or masks in the requested units by reading the underlying raster/vector data referenced by the input handle.\n- PlanRoutes(question: str, work: str, objective: 'nearest_reachable_shelter'|'candidate_routes'|'overlay_routes', avoid: dict) # Recompute route metrics and composite edge costs from declared packaged geometry/road products, then select eligible fastest, safest-weighted, and balanced candidates; this worker does not run a new graph search.\n- PrepareScene(question: str, inputs: dict, mode: 'forest'|'hazard'|'xbd_damage'|'sar_flood'|'routing'|'scene_compare'|'forest_change'|'forest_timeseries'|'forest_false_positive'|'baseline_canopy'|'forest_loss_history'|'forest_cross_modal'|'sar_change'|'wildfire_burn'|'forest_loss'|'built_up_gain'|'drought_anomaly'|'pollution_peak'|'multisource_hazard'|'wildfire_burn_exposure'|'forest_loss_exposure'|'built_up_gain_exposure'|'drought_anomaly_exposure'|'pollution_peak_exposure'|'xbd_grid'|'xbd_quadrants'|'xbd_scene_summary'|'flood_safe_routing'|'sar_flood_reference'|'sar_flood_diagnostic', checks?: dict) # Build an aligned work context from one or more opened scene handles. It checks handle types and records the processing mode before downstream analytics.\n- RankRegions(question: str, work: str, by: 'severe_footprint_area_m2'|'severe_area_m2'|'severe_damage_count'|'severe_count'|'normalized_damage_severity'|'normalized_severity'|'severity_score'|'severity_score_normalized'|'flood_percentage'|'flood_pixels'|'flood_area', candidates: 'grid_4x4'|'quadrants_2x2', top_k?: int) # Rank regions, components, grid cells, quadrants, or route candidates by the requested score.\n- ReadData(question: str, input: str, role?: 'pre_post'|'hansen'|'ndvi_nbr'|'vh'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'hospitals_and_schools'|'highways'|'nearest_highway'|'population_in_footprint'|'schools'|'population'|'post_disaster'|'buildings'|'target_classes'|'scene_a'|'scene_b'|'roads_shelters_flood'|'roads_shelters_flood_rgb'|'primary'|'known_water_reference', time?: 'event'|'event_window'|'pre_post_epochs'|'2001_2023', options?: dict) # Open a named input handle for the current question, validate its local artifact paths, and return a scene handle containing the discovered raster/vector/image/report refs. The evaluator must initialize the session first with code/scripts/initialize_tool_session.py; case contents are not agent-visible tool arguments.\n- RenderMap(question: str, work: str, layers: dict, style: 'route_overlay'|'flood_diagnostic') # Render a fresh run-scoped map/image artifact and validation manifest from the current analytical handle and exactly the requested declared layers; prepared answer overlays and unrequested context layers are not retrieved.\n- ScoreHazard(question: str, work: str, hazard: 'forest_disturbance'|'wildfire'|'deforestation'|'urban_expansion'|'drought'|'air_pollution'|'building_damage', scale: 'vh_delta_severity'|'question_defined_class'|'benchmark_relative_column_quartile'|'impact_footprint'|'spatial_concentration') # Score hazard class, severity, concentration, or impact footprint from a prepared/analytic handle.\n"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\nREGISTERED INPUTS\n- question_1 [question] - Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - Required JRC known-water context for overlap/outside-reference measurement.\n\nTERMINAL CONTRACT\nThe runtime publishes ANSWER_JSON:answer_1 only after worker evidence satisfies the hidden declared answer schema.\n\nSTATE\nNo tools have been called yet.\n\nINSTRUCTION\nProduce a Plan action first, unless the answer is already fully visible. Then use tools one at a time."}, {"role": "assistant", "content": "{\"thought\":\"Plan the evidence path first so tool calls stay grounded in visible handles and requested outputs.\",\"action\":{\"tool\":\"Plan\",\"goal\":\"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\",\"steps\":[{\"id\":\"s1\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\",\"needs\":[\"question_1\",\"sar_chip_1\",\"water_reference_1\"]},{\"id\":\"s2\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\",\"needs\":[\"question_1\",\"objects_1\"]},{\"id\":\"s3\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\",\"needs\":[\"question_1\",\"flood_report_1\"]},{\"id\":\"s4\",\"objective\":\"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\",\"needs\":[\"question_1\",\"compare_1\"]}]}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"status\": \"none\"}\n\nTURN\n1\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `sar_chip_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"sar_chip_1\",\"role\":\"primary\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_1`\"\n- latest_handle = \"scene_1\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"sar_chip_1\", \"question\": \"question_1\", \"role\": \"primary\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_1\", \"latest_summary\": \"opened input as `scene_1`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_1_summary\": \"opened input as `scene_1`\"}, \"layers\": []}, \"text\": \"opened input as `scene_1`\", \"tool\": \"ReadData\"}\n\nTURN\n2\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Need a verified scene handle for `water_reference_1` before any downstream evidence step.\",\"action\":{\"tool\":\"ReadData\",\"args\":{\"question\":\"question_1\",\"input\":\"water_reference_1\",\"role\":\"known_water_reference\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"opened input as `scene_2`\"\n- latest_handle = \"scene_2\"\n- latest_tool = \"ReadData\"\n- latest_tool_ok = true\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"input\": \"water_reference_1\", \"question\": \"question_1\", \"role\": \"known_water_reference\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"scene_2\", \"latest_summary\": \"opened input as `scene_2`\", \"latest_tool\": \"ReadData\", \"latest_tool_ok\": true, \"scene_2_summary\": \"opened input as `scene_2`\"}, \"layers\": []}, \"text\": \"opened input as `scene_2`\", \"tool\": \"ReadData\"}\n\nTURN\n3\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Opened scenes are ready; combine {\\\"sar\\\": \\\"scene_1\\\", \\\"water\\\": \\\"scene_2\\\"} into a `sar_flood_diagnostic` work context with units, masks, and metadata preserved.\",\"action\":{\"tool\":\"PrepareScene\",\"args\":{\"question\":\"question_1\",\"inputs\":{\"sar\":\"scene_1\",\"water\":\"scene_2\"},\"mode\":\"sar_flood_diagnostic\",\"checks\":{\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"prepared reusable work context `work_1`\"\n- latest_handle = \"work_1\"\n- latest_tool = \"PrepareScene\"\n- latest_tool_ok = true\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"checks\": {\"grid\": \"4x4\"}, \"inputs\": {\"sar\": \"scene_1\", \"water\": \"scene_2\"}, \"mode\": \"sar_flood_diagnostic\", \"question\": \"question_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_handle\": \"work_1\", \"latest_summary\": \"prepared reusable work context `work_1`\", \"latest_tool\": \"PrepareScene\", \"latest_tool_ok\": true, \"work_1_summary\": \"prepared reusable work context `work_1`\"}, \"layers\": []}, \"text\": \"prepared reusable work context `work_1`\", \"tool\": \"PrepareScene\"}\n\nTURN\n4\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"SAR work context is ready; derive flood extent with `s1_min_backscatter_otsu_v1` for mask/component evidence.\",\"action\":{\"tool\":\"MapFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"work_1\",\"method\":\"s1_min_backscatter_otsu_v1\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"mapped flood `flood_1`\"\n- latest_handle = \"flood_1\"\n- latest_tool = \"MapFlood\"\n- latest_tool_ok = true\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"method\": \"s1_min_backscatter_otsu_v1\", \"question\": \"question_1\", \"work\": \"work_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_1_answer_ref\": \"ANSWER_JSON:flood_1\", \"flood_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"generated_from_declared_sentinel1\", \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_pe...\n\nTURN\n5\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Extract `flood_polygons` from the prepared context using the requested filters.\",\"action\":{\"tool\":\"ExtractObjects\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_1\",\"target\":\"flood_polygons\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"extracted objects `objects_1`\"\n- latest_handle = \"objects_1\"\n- latest_tool = \"ExtractObjects\"\n- latest_tool_ok = true\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"question\": \"question_1\", \"target\": \"flood_polygons\", \"work\": \"flood_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:objects_1\", \"latest_handle\": \"objects_1\", \"latest_summary\": \"extracted objects `objects_1`\", \"latest_tool\": \"ExtractObjects\", \"latest_tool_ok\": true, \"objects_1_answer_ref\": \"ANSWER_JSON:objects_1\", \"objects_1_evidence\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.07...\n\nTURN\n6\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Flood evidence is ready; report metrics {\\\"percentage\\\": true, \\\"area\\\": true, \\\"components\\\": true, \\\"polygons\\\": true, \\\"grid\\\": \\\"4x4\\\"}.\",\"action\":{\"tool\":\"DescribeFlood\",\"args\":{\"question\":\"question_1\",\"work\":\"objects_1\",\"metrics\":{\"percentage\":true,\"area\":true,\"components\":true,\"polygons\":true,\"grid\":\"4x4\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"described flood `flood_report_1`\"\n- latest_handle = \"flood_report_1\"\n- latest_tool = \"DescribeFlood\"\n- latest_tool_ok = true\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"metrics\": {\"area\": true, \"components\": true, \"grid\": \"4x4\", \"percentage\": true, \"polygons\": true}, \"question\": \"question_1\", \"work\": \"objects_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"flood_report_1_answer_ref\": \"ANSWER_JSON:flood_report_1\", \"flood_report_1_evidence\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fra...\n\nTURN\n7\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Rank `grid_4x4` by `flood_percentage` so the top item has explicit support.\",\"action\":{\"tool\":\"RankRegions\",\"args\":{\"question\":\"question_1\",\"work\":\"flood_report_1\",\"by\":\"flood_percentage\",\"candidates\":\"grid_4x4\",\"top_k\":3}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"ranked candidates `rank_1`\"\n- latest_handle = \"rank_1\"\n- latest_tool = \"RankRegions\"\n- latest_tool_ok = true\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- scene_1_summary = \"opened input as `scene_1`\"\nAvailable answer references:\n (none; continue gathering evidence)\n\nLAST OBSERVATION\n{\"args\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"question\": \"question_1\", \"top_k\": 3, \"work\": \"flood_report_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"latest_answer_ref\": \"ANSWER_JSON:rank_1\", \"latest_handle\": \"rank_1\", \"latest_summary\": \"ranked candidates `rank_1`\", \"latest_tool\": \"RankRegions\", \"latest_tool_ok\": true, \"rank_1_answer_ref\": \"ANSWER_JSON:rank_1\", \"rank_1_evidence\": {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645, \"fraction_of_total_flood\": 0.079846, \"row\": 3, \"valid_pixels\": 16384}, {\"cell_id\": \"R1_C2\", \"col\": 2, \"flood_percentage\": 44.73877, \"flood_pixels\": 7330, \"fraction_of_total_flood\": 0.076556, \"row\": 1, \"valid_pixels\": 16384}]}, \"rank_1_payload\": {\"all_candidates\":...\n\nTURN\n8\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Prepared sources must be checked against each other; compare {\\\"reference\\\": \\\"known_water\\\", \\\"criterion\\\": \\\"diagnostic_overlap\\\"} and keep the support metrics.\",\"action\":{\"tool\":\"CompareSources\",\"args\":{\"question\":\"question_1\",\"work\":\"rank_1\",\"compare\":{\"reference\":\"known_water\",\"criterion\":\"diagnostic_overlap\"}}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"compared sources `compare_1`\"\n- latest_handle = \"compare_1\"\n- latest_tool = \"CompareSources\"\n- latest_tool_ok = true\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- work_1_summary = \"prepared reusable work context `work_1`\"\n- scene_2_summary = \"opened input as `scene_2`\"\n- ... 1 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c9d3b5662f3]\n\nLAST OBSERVATION\n{\"args\": {\"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"question\": \"question_1\", \"work\": \"rank_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"compare_1_answer_ref\": \"ANSWER_JSON:compare_1\", \"compare_1_evidence\": {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}, \"compare_1_payload\": {\"available\": true, \"compare\": {\"criterion\": \"diagnostic_overlap\", \"reference\": \"known_water\"}, \"mask_summary\": {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"method\": \"s1_min_backscatter_otsu_v1\", \"polygon_count\": 2703, \"top_3_grid_cells\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage...\n\nTURN\n9\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"Map-ready evidence is prepared; render layers with style `flood_diagnostic` for the final artifact.\",\"action\":{\"tool\":\"RenderMap\",\"args\":{\"question\":\"question_1\",\"work\":\"compare_1\",\"layers\":{\"sar\":true,\"flood_mask\":true,\"components\":true,\"grid\":true},\"style\":\"flood_diagnostic\"}}}"}, {"role": "user", "content": "QUESTION\nGenerate a complete SAR flood diagnostic for chip India_773682. Create a prediction-mask artifact, 8-neighbour polygon GeoJSON, exact-grid area and component summary, largest-component geometry, full 4x4 grid and top-three ranking, JRC known-water overlap, and a fresh SAR/flood/component/grid visualization. Return the declared structured summary.\n\n\nTERMINAL CONTRACT\nUse FinalAnswer only with a reference currently listed under Available answer references.\n\nCURRENT PLAN\n{\"goal\": \"Resolve the Sentinel-1 SAR flood / diagnostic E task using the declared handles and answer schema.\", \"steps\": [{\"id\": \"s1\", \"needs\": [\"question_1\", \"sar_chip_1\", \"water_reference_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: prepare SAR flood evidence from sar_chip_1, water_reference_1. Verify the declared sources, preserve spatial/grid context, and expose flood extent plus component/area support for later description or ranking.\"}, {\"id\": \"s2\", \"needs\": [\"question_1\", \"objects_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: describe prepared flood evidence in the requested terms: area/percentage, components, grid concentration, or known-water split.\"}, {\"id\": \"s3\", \"needs\": [\"question_1\", \"flood_report_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: compare the prepared sources or references for the requested phenomenon. Report agreement or disagreement with the key support metrics.\"}, {\"id\": \"s4\", \"needs\": [\"question_1\", \"compare_1\"], \"objective\": \"Sentinel-1 SAR flood / diagnostic E: render the requested map or overlay artifact from prepared evidence. Report the artifact handle/path and layer summary.\"}], \"tool\": \"Plan\"}\n\nSHARED MEMORY\nArtifacts:\n- question_1 [question] - question_1: Current natural-language question and schema handle.\n- sar_chip_1 [sar_chip] - sar_chip_1: Required finite-nodata Sentinel-1 VV/VH raster used by the prediction worker.\n- water_reference_1 [water_reference] - water_reference_1: Required JRC known-water context for overlap/outside-reference measurement.\nFields:\n- latest_summary = \"rendered map `image_1`\"\n- latest_handle = \"image_1\"\n- latest_tool = \"RenderMap\"\n- latest_tool_ok = true\n- image_1_evidence = {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2836_6aaadd11bab3__27e6eef7b9/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}\n- image_1_summary = \"rendered map `image_1`\"\n- compare_1_evidence = {\"mask_summary\": {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709}, \"outside_reference_pct_of_flood\": 99.696074, \"outside_reference_pixels\": 95456, \"overlap_pct_of_flood\": 0.303926, \"overlap_pixels\": 291}\n- compare_1_summary = \"compared sources `compare_1`\"\n- rank_1_evidence = {\"by\": \"flood_percentage\", \"candidates\": \"grid_4x4\", \"top_k\": 3, \"top_ranked\": [{\"cell_id\": \"R4_C2\", \"col\": 2, \"flood_percentage\": 48.175049, \"flood_pixels\": 7893, \"fraction_of_total_flood\": 0.082436, \"row\": 4, \"valid_pixels\": 16384}, {\"cell_id\": \"R3_C2\", \"col\": 2, \"flood_percentage\": 46.661377, \"flood_pixels\": 7645...\n- rank_1_summary = \"ranked candidates `rank_1`\"\n- flood_report_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"computed_flood_pixel_count\": 95747, \"computed_valid_pixel_count\": 262144, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709...\n- flood_report_1_summary = \"described flood `flood_report_1`\"\n- objects_1_evidence = {\"component_count\": 2703, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"largest_component_pixel_count\": 33287, \"object_count\": 2703, \"polygon_count\": 2703, \"target\": \"flood_polygons\", \"to...\n- objects_1_summary = \"extracted objects `objects_1`\"\n- flood_1_evidence = {\"chip_id\": \"India_773682\", \"component_count\": 2703, \"computed_flood_area_km2\": 8.519527, \"computed_flood_area_m2\": 8519527.362, \"computed_flood_percentage\": 36.524582, \"concentration_score\": 0.082436, \"largest_component_area_m2\": 2961927.709, \"largest_component_centroid\": [92.925224, 26.697272], \"mask_source\": \"gen...\n- flood_1_summary = \"mapped flood `flood_1`\"\n- ... 3 older fields hidden\nAvailable answer references:\n ANSWER_JSON:answer_1 [schema-valid; sha256=4c9d3b5662f3]\n\nLAST OBSERVATION\n{\"args\": {\"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"question\": \"question_1\", \"style\": \"flood_diagnostic\", \"work\": \"compare_1\"}, \"ok\": true, \"produced\": {\"artifacts\": [], \"fields\": {\"image_1_answer_ref\": \"ANSWER_JSON:image_1\", \"image_1_evidence\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2836_6aaadd11bab3__27e6eef7b9/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"style\": \"flood_diagnostic\"}, \"image_1_payload\": {\"artifact\": \"\", \"exists\": true, \"image_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2836_6aaadd11bab3__27e6eef7b9/sentinel1_prediction_overlay.png\", \"layers\": {\"components\": true, \"flood_mask\": true, \"grid\": true, \"sar\": true}, \"render_manifest_path\": \"generated-artifact://49_publication_final/full_2921/artifacts/replay_2836_6aaadd11bab3__27e6eef7b9/sent...\n\nTURN\n21\n\nINSTRUCTION\nChoose the next justified action: call one tool, or FinalAnswer if the evidence is complete."}, {"role": "assistant", "content": "{\"thought\":\"The necessary evidence is now in shared memory; answer from the tool results without inventing new values.\",\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}], "source": "ourdata/sen1_hand_final_hidden_eval_5q", "export_source": "geodisaster_test", "idx": 2836, "id": "sen1_flood/India_773682/E", "dataset_id": "sen1_flood/India_773682/E", "sample_id": "India_773682", "question_type": "E", "final_split": "test", "tool_sequence": ["ReadData", "ReadData", "PrepareScene", "MapFlood", "ExtractObjects", "DescribeFlood", "RankRegions", "CompareSources", "RenderMap"], "training_weight": 1.0, "training_weight_contract": "inverse_complete_trajectory_multiplicity_v1", "final_answer_ref": "ANSWER_JSON:answer_1", "resolved_answer_sha256": "4c9d3b5662f3a1da1fbcc8fccc01955768e22635ab8d3e2bcbcfdae1c1d5259d"}